From Raw Data to Real Business Impact: Mastering Power BI and Microsoft Fabric with Thummalacherla Krishnakanth

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Yeah, welcome everybody.

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Back to the MC65 podcast where we talk about all products,

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stuff around the Microsoft, the future.

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Yeah, and interviewing experts today's guest,

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Chris Laka is a Microsoft MVP,

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Senior Power BI developer at Questblow.

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Through his career, he has divided,

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and the price analytics solutions for organization,

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including Exxon mobile, Rolleroy, Siemens,

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Gamza, I think business does for broad data into meaningful insights.

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Real exploring everything from Power BI architecture,

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DAX and optimizing fabric, snowflake, data bricks,

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AI analytics, career advice, and so on.

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So yeah, welcome, Chris Laka.

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Thank you for being taking time to be here on the MC65 podcast.

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Yeah, thank you. Thank you, Malcolm.

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Thank you for this opportunity.

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Hi, everyone. Thank you for joining here here.

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So myself, Krishna come.

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I'm joining in from Bangalore, India.

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So overall, I do have a seven years of experience

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and currently working for one of the service based firm,

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organization for one of the oil and gas projects.

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And I'm also a fabric super user.

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For which I got the recognition for the data community,

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for all the contributions.

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So yeah, that's about my short introduction.

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Yeah, for listeners, you don't know you.

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How have you started your career and what especially brings you to the data world

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in the Microsoft ecosystem?

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Yeah, so initially I started my career as an electric engineer.

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So that's where I used to start my career.

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And then I already from my childhood, I have the

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weaving of the rocket launches basically where I started my childhood

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from the Indian Space Research Foundation,

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a boundary where all the family of ice row,

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where the rocket launches will be keep launching on.

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So where my father also the part of the launching activities.

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So that's where I used to think with respect to analytics part,

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where that's where my during 2020, 2020,

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2020, I started thinking towards the analytics part,

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how can I come up with a gap?

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So can I come up with the visuals?

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Like how many launches has been getting in order in each year,

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year, on year?

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So how many are getting success?

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How many are getting failures?

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So that's where my thinking has started towards the visualization

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from the electrical engineering.

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So that's where I made the transition.

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Awesome. It's interesting.

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Yeah, a lot of people say now with a eye, power,

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or other, a lot of people say,

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that are people say, excellent depth because we have power,

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be eye now, a lot of people say, okay, power be eye that because we have a eye.

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What's your view on this?

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Yeah, yeah, because so most probably I don't think like,

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we need to be always with the basic parts and the fundamentals

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with respect to the power be this time question.

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I agree partially with things, what you're saying.

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So so coming into the AI picture,

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it completely takes a lot of time down the line if we take it.

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So it takes more time where we can see the incorporating of the AI things directly in the port.

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So as of now, a developer can successfully learn if he or she is interested

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towards working with the public dashboard development.

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Yeah, I think, yeah, power be eye it's for me, it's the coolest product I work,

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especially in building reports. It's cooler than I don't know.

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Yeah, SAP, SAP, Einstein and so on.

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But there's a new product. Have you have you also try out the fabric apps?

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Fabric apps, I didn't get a chance, Mirko, but I started exploring into the fabric workloads

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with respect to the data, like, Oh, where else real time.

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Oh, that's that's cool.

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Data flow, yeah.

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What will you say, what does power be eye better than traditional reporting tools?

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Like, I don't know, Excel companies use.

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Yeah. So mostly when companies, it comes like there are companies that are using still with respect to the Excel and the tab.

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So when it comes to the Excel, we can where we can able to handle not use your limit of data.

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So there will be a certain restrictions.

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So they can access to bring in only limited records into the Excel.

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So that's where we can go with the then when there is a huge volume of data, we can go with the power be eye.

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And it comes to the tab.

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There is a licensing part which is a bit more cost.

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So that's where when it comes to power be eye, for example, if I take a pro license, it will be around $10.

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If I offer premium for user, it will be around $20.

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So that's where the licensing part when it comes to the power be ecosystem.

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And also we are seeing monthly monthly updates, one by one.

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So that's where I feel the coolest thing to go for you.

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It is utilizing the power be application.

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Oh, yeah, updates.

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Microsoft does do a lot of updates and renaming and so on.

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What was the coolest update you see in power be eye?

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Yeah, in the reason in the recent point of time, I can see with respect to the date slicer with the broad with respect to the last grand up to last seven weeks last 30 days.

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So this kind of update will definitely will give the many end users where they can know need to every time ask for the developer.

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So by default, when we have those options in the top down, we can able to where basically the users can able to check it down like what the inside they are looking for.

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And you, I think you heavily use power be eye.

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So what is the, or what is the most mistakes people doing when when they're using power be eye?

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Yeah, so most mistakes which I noticed most mostly is basically when pulling the data from the data sources.

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So basically most of people there are directly pulling it into the reporting, instead of where they need to check for the data quality, how the data qualities in each column, how the quality is like for example, if there is a date column, there will be an actual data.

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And if there is a textual column, they will be having a certain date call date related values.

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So they, these are all they have to ensure that they are ensuring that data quality is accurate and they have to check for if there are any errors, they need to ensure that there are targeting result before pulling it into directly into the reporting.

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So they need to result all these issues in the parkory level itself, where we have the parkory engine inside the power by desktop.

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And also they have to ensure the accurate date types that they are giving.

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So by default, probably they will pick some of the other data types.

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So those things also they have to take care and then they have to ensure that right data modeling there before going on to the visualization section, they need to ensure the right data modeling is given between the dimension table and fact tables and also the cross filter direction also the matters.

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So they have to ensure always the direction is flowing from the dimension table to fact table.

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That means you do the downstream, but not into the upstream. So these are the certain tips where most most people makes the mistakes without following these and.

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So I would suggest these things to be followed so that they can ensure the values are which they are showcasing to the users are accurate without having any ambiguity.

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I think when we look at Lincoln and the power power BI groups, there it's a new, I think not really new, but I think a bus word is scalable power BI architecture.

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So what does it mean a scalable power BI architecture?

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What kind of architecture level you are just want to confirm it go.

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I read a lot of times as to people write this.

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What what means especially the scalable is is not all a power BI architecture scalable or is something special I have to do that I can say it's scalable.

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It depends actually depends on the project and the data that where team is getting so that's where the architecture comes into the picture and he where Arctic has to ensure how and what kind of licenses respect fabric if you have so what kind of fabric license we today either 64 SKs, stockkeeping unit or more than that we need to go depending on the work towards we are using.

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So that's where it depends the scalability depends on.

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It depends on each project.

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Yeah, I think there are I think we have the VM and looking the football games and so on and and yeah, I think also when we talk about power BI and architecture there are two teams two big teams to four favorites.

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The one side is the staff schema on the other side play the slow flake schema.

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When do you choose each.

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I lost your voice in between medical can you.

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Yeah, sorry, sorry, sorry.

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We have star schema versus snowflake schema. When do you choose each.

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So for example, when we have a denormal stables like for example, if I go with if I want to go with a star schema so where my dimension tables are all directly connected with the fact tables so this schema is nothing but where we'll be having a denormal tables.

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So which is also a best performance wise if we go, which is a best to utilize as a best practice as well.

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So when I want to when I have more number of tables like for example, if I have a product category, if I have product sub category in that case my product sub category table is not directly connected to the existing sales fact table.

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So product sub category table will be connected to the product category and then product category table will be getting connected to the sales.

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This is where this schema basically we used to call it as a snowflake schema, which is nothing but where we'll be having a normalized tables.

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So if your requirement is having more number of tables than I would suggest to have a denormal tables to make it like five tables if we have to make it as a three if you have three tables then I would suggest to make it as a one.

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So in the means we will be having a less number of dimension tables getting directly connected to the fact tables so I would suggest to go with the star schema as a best practice.

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And then we also have another yeah versus topic and there it's on the one side it's power query and on the other side is tax.

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What when you use what and why and what's the different between power query and tax.

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Yeah, so if we when we go with the power query engine level like for example if I don't want to unnecessarily create a colored columns in the tax level itself so where I can say that performance can be reduced.

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So if I can go with utilizing the power query itself then that would be very helpful where we can improve the performance rather than going to power query I would suggest to go with data source itself because whatever the queries you want to perform or you want to create few more columns based on the conditional columns conditional certain conditions based on existing columns I would suggest to go and write it in with the data source itself so that we can ensure that a report will not slow down its performance.

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So when there is a chance of where you want to perform kind of unpewitting columns when your data is not in a structured format so so those cases in those scenarios we can go with the using power query engine and where you can perform and if you have a certain tables of similar structure where you have five to six tables then you can go for upending the queries.

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If you want to merge two tables based on a common column for example if I have leak any column I want to merge table here and table B so based on leak any column I can perform the merge transformation.

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So if I have a similar kind of 10 to 12 date columns where I can see in the column level where I have the values so I can go with unpewitting the columns where it would use me the attributes and values.

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So this is how we can utilize the per query engine level in order to perform certain transformations according to our requirements from the data that we are getting in.

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When it comes to the DAX when you want to showcase certain metrics certain key metrics so that's where we can utilize the data analysis expressions which we have by default and we can showcase certain insights based on certain trends, Iran, here month on month using card visual KPI visuals.

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So that's where we can utilize the DAX analysis expressions.

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Yeah, that's good.

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What I often see in or one of my personal problems when the project become bigger power be I getting getting getting slow.

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What's your, what's your favorite optimization techniques here to to hold to make it.

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Yeah, so basically we need to we can open the performance and laser and where we can able to check from each visual level which how much time it is taking more.

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So that's how we can analyze by checking the how much time it is taking with each visual whichever visual is taking more time.

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Then we need to go and check in the data source.

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So what kind of how the column is and what kind of data we have so then we can accordingly we can ensure that switching of import whether we can switch from import to data query so that's where we need to analyze and we need to take certain actions so that we can ensure that the best performance has been achieved where end users will not feel that visuals are loading for more time.

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And a lot of companies I think they're doing giving all people all data is their one point.

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Yeah, we can say that the data set becomes too big.

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So that can become huge if they are adding on a day on a daily basis, Mirko, it gets huge. Yes, so they can best best ways to go with the live connectivity.

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So so that all the dead connectivity so it would be good if we hadn't have the data in the Azure analysis service so that we can utilize for the reporting point of you, if you say.

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And what would you think we have I think we have one side we have the use a management on power be I have to use a management inside the fabric when that you choose what tool for for as a governance to to use here.

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Yeah, governance point of view so now it is like most of the companies and auditions are switching towards the fabric environment in order to build the reports also into the fabric environment itself so I would like I would see like.

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Mostly they did know now we can see the data flow gentle as well as come so where we have a certain.

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When it comes to the licensing part I don't need to take a new licenses actual so if I can go 64 SKU capacity then I would start working on clearing of the data by getting my data into Lakehouse and performing data transformations using data flow gentle to.

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And then I can bring it into my warehouse and I can start performing the reporting I can start building on so that's where I can see when it comes to the power be I am fabric I would see.

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Down the lane we can see most users most organizations will ask us for to start working on with the fabric environment itself.

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And yeah when we talk about governance a lot of companies are especially tools like that thing we have in tune we have.

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Enter and we have view view what roles to display tools play when it comes to to data science on on the Microsoft part.

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Yeah, part of you differently they definitely give us the best security level when it comes to security I would see there is a good scope in the future in where we can able to have the good security when it comes to the data science where they can.

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And then you can also see the data that they are.

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00:19:11,000 --> 00:19:12,000
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204
00:19:12,000 --> 00:19:13,000
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205
00:19:13,000 --> 00:19:14,000
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206
00:19:14,000 --> 00:19:15,000
And then you can see the data that they are.

207
00:19:15,000 --> 00:19:25,000
And then you can see the data that they are.

208
00:19:25,000 --> 00:19:26,000
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209
00:19:26,000 --> 00:19:27,000
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210
00:19:27,000 --> 00:19:28,000
And then you can see the data that they are.

211
00:19:28,000 --> 00:19:29,000
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212
00:19:29,000 --> 00:19:30,000
And then you can see the data that they are.

213
00:19:30,000 --> 00:19:31,000
And then you can see the data that they are.

214
00:19:31,000 --> 00:19:32,000
And then you can see the data that they are.

215
00:19:32,000 --> 00:19:33,000
And then you can see the data that they are.

216
00:19:33,000 --> 00:19:34,000
And then you can see the data that they are.

217
00:19:34,000 --> 00:19:35,000
And then you can see the data that they are.

218
00:19:35,000 --> 00:19:36,000
And then you can see the data that they are.

219
00:19:36,000 --> 00:19:37,000
And then you can see the data that they are.

220
00:19:37,000 --> 00:19:38,000
And then you can see the data that they are.

221
00:19:38,000 --> 00:19:39,000
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222
00:19:39,000 --> 00:19:40,000
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223
00:19:40,000 --> 00:19:50,000
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224
00:19:50,000 --> 00:19:51,000
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225
00:19:51,000 --> 00:19:52,000
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226
00:19:52,000 --> 00:19:53,000
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227
00:19:53,000 --> 00:19:54,000
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228
00:19:54,000 --> 00:19:55,000
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229
00:19:55,000 --> 00:19:56,000
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230
00:19:56,000 --> 00:19:57,000
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231
00:19:57,000 --> 00:19:58,000
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232
00:19:58,000 --> 00:19:59,000
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233
00:19:59,000 --> 00:20:00,000
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234
00:20:00,000 --> 00:20:01,000
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235
00:20:01,000 --> 00:20:02,000
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236
00:20:02,000 --> 00:20:03,000
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237
00:20:03,000 --> 00:20:04,000
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238
00:20:04,000 --> 00:20:05,000
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239
00:20:05,000 --> 00:20:15,000
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240
00:20:15,000 --> 00:20:16,000
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241
00:20:16,000 --> 00:20:17,000
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242
00:20:17,000 --> 00:20:18,000
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243
00:20:18,000 --> 00:20:19,000
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244
00:20:19,000 --> 00:20:20,000
And then you can see the data that they are.

245
00:20:20,000 --> 00:20:21,000
And then you can see the data that they are.

246
00:20:21,000 --> 00:20:22,000
And then you can see the data that they are.

247
00:20:22,000 --> 00:20:23,000
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248
00:20:23,000 --> 00:20:24,000
And then you can see the data that they are.

249
00:20:24,000 --> 00:20:25,000
And then you can see the data that they are.

250
00:20:25,000 --> 00:20:26,000
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251
00:20:26,000 --> 00:20:27,000
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252
00:20:27,000 --> 00:20:28,000
And then you can see the data that they are.

253
00:20:28,000 --> 00:20:29,000
And then you can see the data that they are.

254
00:20:29,000 --> 00:20:30,000
And then you can see the data that they are.

255
00:20:30,000 --> 00:20:40,000
And then you can see the data that they are.

256
00:20:40,000 --> 00:20:41,000
And then you can see the data that they are.

257
00:20:41,000 --> 00:20:42,000
And then you can see the data that they are.

258
00:20:42,000 --> 00:20:43,000
And then you can see the data that they are.

259
00:20:43,000 --> 00:20:44,000
And then you can see the data that they are.

260
00:20:44,000 --> 00:20:45,000
And then you can see the data that they are.

261
00:20:45,000 --> 00:20:46,000
And then you can see the data that they are.

262
00:20:46,000 --> 00:20:47,000
And then you can see the data that they are.

263
00:20:47,000 --> 00:20:48,000
And then you can see the data that they are.

264
00:20:48,000 --> 00:20:49,000
And then you can see the data that they are.

265
00:20:49,000 --> 00:20:50,000
And then you can see the data that they are.

266
00:20:50,000 --> 00:20:51,000
And then you can see the data that they are.

267
00:20:51,000 --> 00:20:52,000
And then you can see the data that they are.

268
00:20:52,000 --> 00:20:53,000
And then you can see the data that they are.

269
00:20:53,000 --> 00:20:54,000
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270
00:20:54,000 --> 00:20:55,000
And then you can see the data that they are.

271
00:20:55,000 --> 00:21:05,000
And then you can see the data that they are.

272
00:21:05,000 --> 00:21:06,000
And then you can see the data that they are.

273
00:21:06,000 --> 00:21:07,000
And then you can see the data that they are.

274
00:21:07,000 --> 00:21:08,000
And then you can see the data that they are.

275
00:21:08,000 --> 00:21:09,000
And then you can see the data that they are.

276
00:21:09,000 --> 00:21:10,000
And then you can see the data that they are.

277
00:21:10,000 --> 00:21:11,000
And then you can see the data that they are.

278
00:21:11,000 --> 00:21:12,000
And then you can see the data that they are.

279
00:21:12,000 --> 00:21:13,000
And then you can see the data that they are.

280
00:21:13,000 --> 00:21:14,000
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281
00:21:14,000 --> 00:21:15,000
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282
00:21:15,000 --> 00:21:16,000
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283
00:21:16,000 --> 00:21:17,000
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284
00:21:17,000 --> 00:21:18,000
And then you can see the data that they are.

285
00:21:18,000 --> 00:21:19,000
And then you can see the data that they are.

286
00:21:19,000 --> 00:21:20,000
And then you can see the data that they are.

287
00:21:20,000 --> 00:21:30,000
And then you can see the data that they are.

288
00:21:30,000 --> 00:21:31,000
And then you can see the data that they are.

289
00:21:31,000 --> 00:21:32,000
And then you can see the data that they are.

290
00:21:32,000 --> 00:21:33,000
And then you can see the data that they are.

291
00:21:33,000 --> 00:21:34,000
And then you can see the data that they are.

292
00:21:34,000 --> 00:21:35,000
And then you can see the data that they are.

293
00:21:35,000 --> 00:21:36,000
And then you can see the data that they are.

294
00:21:36,000 --> 00:21:37,000
And then you can see the data that they are.

295
00:21:37,000 --> 00:21:38,000
And then you can see the data that they are.

296
00:21:38,000 --> 00:21:39,000
And then you can see the data that they are.

297
00:21:39,000 --> 00:21:40,000
And then you can see the data that they are.

298
00:21:40,000 --> 00:21:41,000
And then you can see the data that they are.

299
00:21:41,000 --> 00:21:42,000
And then you can see the data that they are.

300
00:21:42,000 --> 00:21:43,000
And then you can see the data that they are.

301
00:21:43,000 --> 00:21:44,000
And then you can see the data that they are.

302
00:21:44,000 --> 00:21:45,000
And then you can see the data that they are.

303
00:21:45,000 --> 00:21:55,000
And then you can see the data that they are.

304
00:21:55,000 --> 00:21:56,000
And then you can see the data that they are.

305
00:21:56,000 --> 00:21:57,000
And then you can see the data that they are.

306
00:21:57,000 --> 00:21:58,000
And then you can see the data that they are.

307
00:21:58,000 --> 00:21:59,000
And then you can see the data that they are.

308
00:21:59,000 --> 00:22:00,000
And then you can see the data that they are.

309
00:22:00,000 --> 00:22:01,000
And then you can see the data that they are.

310
00:22:01,000 --> 00:22:02,000
And then you can see the data that they are.

311
00:22:02,000 --> 00:22:03,000
And then you can see the data that they are.

312
00:22:03,000 --> 00:22:04,000
And then you can see the data that they are.

313
00:22:04,000 --> 00:22:05,000
And then you can see the data that they are.

314
00:22:05,000 --> 00:22:06,000
And then you can see the data that they are.

315
00:22:06,000 --> 00:22:07,000
And then you can see the data that they are.

316
00:22:07,000 --> 00:22:08,000
And then you can see the data that they are.

317
00:22:08,000 --> 00:22:09,000
And then you can see the data that they are.

318
00:22:09,000 --> 00:22:10,000
And then you can see the data that they are.

319
00:22:10,000 --> 00:22:20,000
And then you can see the data that they are.

320
00:22:20,000 --> 00:22:21,000
And then you can see the data that they are.

321
00:22:21,000 --> 00:22:22,000
And then you can see the data that they are.

322
00:22:22,000 --> 00:22:23,000
And then you can see the data that they are.

323
00:22:23,000 --> 00:22:24,000
And then you can see the data that they are.

324
00:22:24,000 --> 00:22:25,000
And then you can see the data that they are.

325
00:22:25,000 --> 00:22:26,000
And then you can see the data that they are.

326
00:22:26,000 --> 00:22:27,000
And then you can see the data that they are.

327
00:22:27,000 --> 00:22:28,000
And then you can see the data that they are.

328
00:22:28,000 --> 00:22:29,000
And then you can see the data that they are.

329
00:22:29,000 --> 00:22:30,000
And then you can see the data that they are.

330
00:22:30,000 --> 00:22:31,000
And then you can see the data that they are.

331
00:22:31,000 --> 00:22:32,000
And then you can see the data that they are.

332
00:22:32,000 --> 00:22:33,000
And then you can see the data that they are.

333
00:22:33,000 --> 00:22:34,000
And then you can see the data that they are.

334
00:22:34,000 --> 00:22:35,000
And then you can see the data that they are.

335
00:22:35,000 --> 00:22:45,000
And then you can see the data that they are.

336
00:22:45,000 --> 00:22:46,000
And then you can see the data that they are.

337
00:22:46,000 --> 00:22:47,000
And then you can see the data that they are.

338
00:22:47,000 --> 00:22:48,000
And then you can see the data that they are.

339
00:22:48,000 --> 00:22:49,000
And then you can see the data that they are.

340
00:22:49,000 --> 00:22:50,000
And then you can see the data that they are.

341
00:22:50,000 --> 00:22:51,000
And then you can see the data that they are.

342
00:22:51,000 --> 00:22:52,000
And then you can see the data that they are.

343
00:22:52,000 --> 00:22:53,000
And then you can see the data that they are.

344
00:22:53,000 --> 00:22:54,000
And then you can see the data that they are.

345
00:22:54,000 --> 00:22:55,000
And then you can see the data that they are.

346
00:22:55,000 --> 00:22:56,000
And then you can see the data that they are.

347
00:22:56,000 --> 00:22:57,000
And then you can see the data that they are.

348
00:22:57,000 --> 00:22:58,000
And then you can see the data that they are.

349
00:22:58,000 --> 00:22:59,000
And then you can see the data that they are.

350
00:22:59,000 --> 00:23:00,000
And then you can see the data that they are.

351
00:23:00,000 --> 00:23:10,000
And then you can see the data that they are.

352
00:23:10,000 --> 00:23:12,000
And then you can see the data that they are.

353
00:23:12,000 --> 00:23:13,000
And then you can see the data that they are.

354
00:23:13,000 --> 00:23:14,000
And then you can see the data that they are.

355
00:23:14,000 --> 00:23:15,000
And then you can see the data that they are.

356
00:23:15,000 --> 00:23:16,000
And then you can see the data that they are.

357
00:23:16,000 --> 00:23:17,000
And then you can see the data that they are.

358
00:23:17,000 --> 00:23:18,000
And then you can see the data that they are.

359
00:23:18,000 --> 00:23:19,000
And then you can see the data that they are.

360
00:23:19,000 --> 00:23:20,000
And then you can see the data that they are.

361
00:23:20,000 --> 00:23:21,000
And then you can see the data that they are.

362
00:23:21,000 --> 00:23:22,000
And then you can see the data that they are.

363
00:23:22,000 --> 00:23:23,000
And then you can see the data that they are.

364
00:23:23,000 --> 00:23:24,000
And then you can see the data that they are.

365
00:23:24,000 --> 00:23:25,000
And then you can see the data that they are.

366
00:23:25,000 --> 00:23:26,000
And then you can see the data that they are.

367
00:23:26,000 --> 00:23:36,000
And then you can see the data that they are.

368
00:23:36,000 --> 00:23:37,000
And then you can see the data that they are.

369
00:23:37,000 --> 00:23:38,000
And then you can see the data that they are.

370
00:23:38,000 --> 00:23:39,000
And then you can see the data that they are.

371
00:23:39,000 --> 00:23:40,000
And then you can see the data that they are.

372
00:23:40,000 --> 00:23:41,000
And then you can see the data that they are.

373
00:23:41,000 --> 00:23:42,000
And then you can see the data that they are.

374
00:23:42,000 --> 00:23:43,000
And then you can see the data that they are.

375
00:23:43,000 --> 00:23:44,000
And then you can see the data that they are.

376
00:23:44,000 --> 00:23:45,000
And then you can see the data that they are.

377
00:23:45,000 --> 00:23:46,000
And then you can see the data that they are.

378
00:23:46,000 --> 00:23:47,000
And then you can see the data that they are.

379
00:23:47,000 --> 00:23:48,000
And then you can see the data that they are.

380
00:23:48,000 --> 00:23:49,000
And then you can see the data that they are.

381
00:23:49,000 --> 00:23:50,000
And then you can see the data that they are.

382
00:23:50,000 --> 00:23:51,000
And then you can see the data that they are.

383
00:23:51,000 --> 00:24:01,000
And then you can see the data that they are.

384
00:24:01,000 --> 00:24:02,000
And then you can see the data that they are.

385
00:24:02,000 --> 00:24:03,000
And then you can see the data that they are.

386
00:24:03,000 --> 00:24:04,000
And then you can see the data that they are.

387
00:24:04,000 --> 00:24:05,000
And then you can see the data that they are.

388
00:24:05,000 --> 00:24:06,000
And then you can see the data that they are.

389
00:24:06,000 --> 00:24:07,000
And then you can see the data that they are.

390
00:24:07,000 --> 00:24:08,000
And then you can see the data that they are.

391
00:24:08,000 --> 00:24:09,000
And then you can see the data that they are.

392
00:24:09,000 --> 00:24:10,000
And then you can see the data that they are.

393
00:24:10,000 --> 00:24:11,000
And then you can see the data that they are.

394
00:24:11,000 --> 00:24:12,000
And then you can see the data that they are.

395
00:24:12,000 --> 00:24:13,000
And then you can see the data that they are.

396
00:24:13,000 --> 00:24:14,000
And then you can see the data that they are.

397
00:24:14,000 --> 00:24:15,000
And then you can see the data that they are.

398
00:24:15,000 --> 00:24:16,000
And then you can see the data that they are.

399
00:24:16,000 --> 00:24:26,000
And then you can see the data that they are.

400
00:24:26,000 --> 00:24:28,000
And then you can see the data that they are.

401
00:24:28,000 --> 00:24:29,000
And then you can see the data that they are.

402
00:24:29,000 --> 00:24:30,000
And then you can see the data that they are.

403
00:24:30,000 --> 00:24:31,000
And then you can see the data that they are.

404
00:24:31,000 --> 00:24:32,000
And then you can see the data that they are.

405
00:24:32,000 --> 00:24:33,000
And then you can see the data that they are.

406
00:24:33,000 --> 00:24:34,000
And then you can see the data that they are.

407
00:24:34,000 --> 00:24:35,000
And then you can see the data that they are.

408
00:24:35,000 --> 00:24:36,000
And then you can see the data that they are.

409
00:24:36,000 --> 00:24:37,000
And then you can see the data that they are.

410
00:24:37,000 --> 00:24:38,000
And then you can see the data that they are.

411
00:24:38,000 --> 00:24:39,000
And then you can see the data that they are.

412
00:24:39,000 --> 00:24:40,000
And then you can see the data that they are.

413
00:24:40,000 --> 00:24:41,000
And then you can see the data that they are.

414
00:24:41,000 --> 00:24:42,000
And then you can see the data that they are.

415
00:24:42,000 --> 00:24:52,000
And then you can see the data that they are.

416
00:24:52,000 --> 00:24:53,000
And then you can see the data that they are.

417
00:24:53,000 --> 00:24:54,000
And then you can see the data that they are.

418
00:24:54,000 --> 00:24:55,000
And then you can see the data that they are.

419
00:24:55,000 --> 00:24:56,000
And then you can see the data that they are.

420
00:24:56,000 --> 00:24:57,000
And then you can see the data that they are.

421
00:24:57,000 --> 00:24:58,000
And then you can see the data that they are.

422
00:24:58,000 --> 00:24:59,000
And then you can see the data that they are.

423
00:24:59,000 --> 00:25:00,000
And then you can see the data that they are.

424
00:25:00,000 --> 00:25:01,000
And then you can see the data that they are.

425
00:25:01,000 --> 00:25:02,000
And then you can see the data that they are.

426
00:25:02,000 --> 00:25:03,000
And then you can see the data that they are.

427
00:25:03,000 --> 00:25:04,000
And then you can see the data that they are.

428
00:25:04,000 --> 00:25:05,000
And then you can see the data that they are.

429
00:25:05,000 --> 00:25:06,000
And then you can see the data that they are.

430
00:25:06,000 --> 00:25:07,000
And then you can see the data that they are.

431
00:25:07,000 --> 00:25:17,000
And then you can see the data that they are.

432
00:25:17,000 --> 00:25:18,000
And then you can see the data that they are.

433
00:25:18,000 --> 00:25:19,000
And then you can see the data that they are.

434
00:25:19,000 --> 00:25:20,000
And then you can see the data that they are.

435
00:25:20,000 --> 00:25:21,000
And then you can see the data that they are.

436
00:25:21,000 --> 00:25:22,000
And then you can see the data that they are.

437
00:25:22,000 --> 00:25:23,000
And then you can see the data that they are.

438
00:25:23,000 --> 00:25:24,000
And then you can see the data that they are.

439
00:25:24,000 --> 00:25:25,000
And then you can see the data that they are.

440
00:25:25,000 --> 00:25:26,000
And then you can see the data that they are.

441
00:25:26,000 --> 00:25:27,000
And then you can see the data that they are.

442
00:25:27,000 --> 00:25:28,000
And then you can see the data that they are.

443
00:25:28,000 --> 00:25:29,000
And then you can see the data that they are.

444
00:25:29,000 --> 00:25:30,000
And then you can see the data that they are.

445
00:25:30,000 --> 00:25:31,000
And then you can see the data that they are.

446
00:25:31,000 --> 00:25:32,000
And then you can see the data that they are.

447
00:25:32,000 --> 00:25:42,000
And then you can see the data that they are.

448
00:25:42,000 --> 00:25:43,000
And then you can see the data that they are.

449
00:25:43,000 --> 00:25:44,000
And then you can see the data that they are.

450
00:25:44,000 --> 00:25:45,000
And then you can see the data that they are.

451
00:25:45,000 --> 00:25:46,000
And then you can see the data that they are.

452
00:25:46,000 --> 00:25:47,000
And then you can see the data that they are.

453
00:25:47,000 --> 00:25:48,000
And then you can see the data that they are.

454
00:25:48,000 --> 00:25:49,000
And then you can see the data that they are.

455
00:25:49,000 --> 00:25:50,000
And then you can see the data that they are.

456
00:25:50,000 --> 00:25:51,000
And then you can see the data that they are.

457
00:25:51,000 --> 00:25:52,000
And then you can see the data that they are.

458
00:25:52,000 --> 00:25:53,000
And then you can see the data that they are.

459
00:25:53,000 --> 00:25:54,000
And then you can see the data that they are.

460
00:25:54,000 --> 00:25:55,000
And then you can see the data that they are.

461
00:25:55,000 --> 00:25:56,000
And then you can see the data that they are.

462
00:25:56,000 --> 00:25:57,000
And then you can see the data that they are.

463
00:25:57,000 --> 00:26:07,000
And then you can see the data that they are.

464
00:26:07,000 --> 00:26:09,000
And then you can see the data that they are.

465
00:26:09,000 --> 00:26:10,000
And then you can see the data that they are.

466
00:26:10,000 --> 00:26:11,000
And then you can see the data that they are.

467
00:26:11,000 --> 00:26:12,000
And then you can see the data that they are.

468
00:26:12,000 --> 00:26:13,000
And then you can see the data that they are.

469
00:26:13,000 --> 00:26:14,000
And then you can see the data that they are.

470
00:26:14,000 --> 00:26:15,000
And then you can see the data that they are.

471
00:26:15,000 --> 00:26:16,000
And then you can see the data that they are.

472
00:26:16,000 --> 00:26:17,000
And then you can see the data that they are.

473
00:26:17,000 --> 00:26:18,000
And then you can see the data that they are.

474
00:26:18,000 --> 00:26:19,000
And then you can see the data that they are.

475
00:26:19,000 --> 00:26:20,000
And then you can see the data that they are.

476
00:26:20,000 --> 00:26:21,000
And then you can see the data that they are.

477
00:26:21,000 --> 00:26:22,000
And then you can see the data that they are.

478
00:26:22,000 --> 00:26:23,000
And then you can see the data that they are.

479
00:26:23,000 --> 00:26:33,000
And then you can see the data that they are.

480
00:26:33,000 --> 00:26:34,000
And then you can see the data that they are.

481
00:26:34,000 --> 00:26:35,000
And then you can see the data that they are.

482
00:26:35,000 --> 00:26:36,000
And then you can see the data that they are.

483
00:26:36,000 --> 00:26:37,000
And then you can see the data that they are.

484
00:26:37,000 --> 00:26:38,000
And then you can see the data that they are.

485
00:26:38,000 --> 00:26:39,000
And then you can see the data that they are.

486
00:26:39,000 --> 00:26:40,000
And then you can see the data that they are.

487
00:26:40,000 --> 00:26:41,000
And then you can see the data that they are.

488
00:26:41,000 --> 00:26:42,000
And then you can see the data that they are.

489
00:26:42,000 --> 00:26:43,000
And then you can see the data that they are.

490
00:26:43,000 --> 00:26:44,000
And then you can see the data that they are.

491
00:26:44,000 --> 00:26:45,000
And then you can see the data that they are.

492
00:26:45,000 --> 00:26:46,000
And then you can see the data that they are.

493
00:26:46,000 --> 00:26:47,000
And then you can see the data that they are.

494
00:26:47,000 --> 00:26:48,000
And then you can see the data that they are.

495
00:26:48,000 --> 00:26:58,000
And then you can see the data that they are.

496
00:26:58,000 --> 00:26:59,000
And then you can see the data that they are.

497
00:26:59,000 --> 00:27:00,000
And then you can see the data that they are.

498
00:27:00,000 --> 00:27:01,000
And then you can see the data that they are.

499
00:27:01,000 --> 00:27:02,000
And then you can see the data that they are.

500
00:27:02,000 --> 00:27:03,000
And then you can see the data that they are.

501
00:27:03,000 --> 00:27:04,000
And then you can see the data that they are.

502
00:27:04,000 --> 00:27:05,000
And then you can see the data that they are.

503
00:27:05,000 --> 00:27:06,000
And then you can see the data that they are.

504
00:27:06,000 --> 00:27:07,000
And then you can see the data that they are.

505
00:27:07,000 --> 00:27:08,000
And then you can see the data that they are.

506
00:27:08,000 --> 00:27:09,000
And then you can see the data that they are.

507
00:27:09,000 --> 00:27:10,000
And then you can see the data that they are.

508
00:27:10,000 --> 00:27:11,000
And then you can see the data that they are.

509
00:27:11,000 --> 00:27:12,000
And then you can see the data that they are.

510
00:27:12,000 --> 00:27:13,000
And then you can see the data that they are.

511
00:27:13,000 --> 00:27:23,000
And then you can see the data that they are.

512
00:27:23,000 --> 00:27:24,000
And then you can see the data that they are.

513
00:27:24,000 --> 00:27:25,000
And then you can see the data that they are.

514
00:27:25,000 --> 00:27:26,000
And then you can see the data that they are.

515
00:27:26,000 --> 00:27:27,000
And then you can see the data that they are.

516
00:27:27,000 --> 00:27:28,000
And then you can see the data that they are.

517
00:27:28,000 --> 00:27:29,000
And then you can see the data that they are.

518
00:27:29,000 --> 00:27:30,000
And then you can see the data that they are.

519
00:27:30,000 --> 00:27:31,000
And then you can see the data that they are.

520
00:27:31,000 --> 00:27:32,000
And then you can see the data that they are.

521
00:27:32,000 --> 00:27:33,000
And then you can see the data that they are.

522
00:27:33,000 --> 00:27:34,000
And then you can see the data that they are.

523
00:27:34,000 --> 00:27:35,000
And then you can see the data that they are.

524
00:27:35,000 --> 00:27:36,000
And then you can see the data that they are.

525
00:27:36,000 --> 00:27:37,000
And then you can see the data that they are.

526
00:27:37,000 --> 00:27:38,000
And then you can see the data that they are.

527
00:27:38,000 --> 00:27:48,000
And then you can see the data that they are.

528
00:27:48,000 --> 00:27:49,000
And then you can see the data that they are.

529
00:27:49,000 --> 00:27:50,000
And then you can see the data that they are.

530
00:27:50,000 --> 00:27:51,000
And then you can see the data that they are.

531
00:27:51,000 --> 00:27:52,000
And then you can see the data that they are.

532
00:27:52,000 --> 00:27:53,000
And then you can see the data that they are.

533
00:27:53,000 --> 00:27:54,000
And then you can see the data that they are.

534
00:27:54,000 --> 00:27:55,000
And then you can see the data that they are.

535
00:27:55,000 --> 00:27:56,000
And then you can see the data that they are.

536
00:27:56,000 --> 00:27:57,000
And then you can see the data that they are.

537
00:27:57,000 --> 00:27:58,000
And then you can see the data that they are.

538
00:27:58,000 --> 00:27:59,000
And then you can see the data that they are.

539
00:27:59,000 --> 00:28:00,000
And then you can see the data that they are.

540
00:28:00,000 --> 00:28:01,000
And then you can see the data that they are.

541
00:28:01,000 --> 00:28:02,000
And then you can see the data that they are.

542
00:28:02,000 --> 00:28:03,000
And then you can see the data that they are.

543
00:28:03,000 --> 00:28:13,000
And then you can see the data that they are.

544
00:28:13,000 --> 00:28:15,000
And then you can see the data that they are.

545
00:28:15,000 --> 00:28:16,000
And then you can see the data that they are.

546
00:28:16,000 --> 00:28:17,000
And then you can see the data that they are.

547
00:28:17,000 --> 00:28:18,000
And then you can see the data that they are.

548
00:28:18,000 --> 00:28:19,000
And then you can see the data that they are.

549
00:28:19,000 --> 00:28:20,000
And then you can see the data that they are.

550
00:28:20,000 --> 00:28:21,000
And then you can see the data that they are.

551
00:28:21,000 --> 00:28:22,000
And then you can see the data that they are.

552
00:28:22,000 --> 00:28:23,000
And then you can see the data that they are.

553
00:28:23,000 --> 00:28:24,000
And then you can see the data that they are.

554
00:28:24,000 --> 00:28:25,000
And then you can see the data that they are.

555
00:28:25,000 --> 00:28:26,000
And then you can see the data that they are.

556
00:28:26,000 --> 00:28:27,000
And then you can see the data that they are.

557
00:28:27,000 --> 00:28:28,000
And then you can see the data that they are.

558
00:28:28,000 --> 00:28:29,000
And then you can see the data that they are.

559
00:28:29,000 --> 00:28:39,000
And then you can see the data that they are.

560
00:28:39,000 --> 00:28:41,000
And then you can see the data that they are.

561
00:28:41,000 --> 00:28:42,000
And then you can see the data that they are.

562
00:28:42,000 --> 00:28:43,000
And then you can see the data that they are.

563
00:28:43,000 --> 00:28:44,000
And then you can see the data that they are.

564
00:28:44,000 --> 00:28:45,000
And then you can see the data that they are.

565
00:28:45,000 --> 00:28:46,000
And then you can see the data that they are.

566
00:28:46,000 --> 00:28:47,000
And then you can see the data that they are.

567
00:28:47,000 --> 00:28:48,000
And then you can see the data that they are.

568
00:28:48,000 --> 00:28:49,000
And then you can see the data that they are.

569
00:28:49,000 --> 00:28:50,000
And then you can see the data that they are.

570
00:28:50,000 --> 00:28:51,000
And then you can see the data that they are.

571
00:28:51,000 --> 00:28:52,000
And then you can see the data that they are.

572
00:28:52,000 --> 00:28:53,000
And then you can see the data that they are.

573
00:28:53,000 --> 00:28:54,000
And then you can see the data that they are.

574
00:28:54,000 --> 00:28:55,000
And then you can see the data that they are.

575
00:28:55,000 --> 00:29:05,000
And then you can see the data that they are.

576
00:29:05,000 --> 00:29:07,000
And then you can see the data that they are.

577
00:29:07,000 --> 00:29:08,000
And then you can see the data that they are.

578
00:29:08,000 --> 00:29:09,000
And then you can see the data that they are.

579
00:29:09,000 --> 00:29:10,000
And then you can see the data that they are.

580
00:29:10,000 --> 00:29:11,000
And then you can see the data that they are.

581
00:29:11,000 --> 00:29:12,000
And then you can see the data that they are.

582
00:29:12,000 --> 00:29:13,000
And then you can see the data that they are.

583
00:29:13,000 --> 00:29:14,000
And then you can see the data that they are.

584
00:29:14,000 --> 00:29:15,000
And then you can see the data that they are.

585
00:29:15,000 --> 00:29:16,000
And then you can see the data that they are.

586
00:29:16,000 --> 00:29:17,000
And then you can see the data that they are.

587
00:29:17,000 --> 00:29:18,000
And then you can see the data that they are.

588
00:29:18,000 --> 00:29:19,000
And then you can see the data that they are.

589
00:29:19,000 --> 00:29:20,000
And then you can see the data that they are.

590
00:29:20,000 --> 00:29:21,000
And then you can see the data that they are.

591
00:29:21,000 --> 00:29:31,000
And then you can see the data that they are.

592
00:29:31,000 --> 00:29:33,000
And then you can see the data that they are.

593
00:29:33,000 --> 00:29:34,000
And then you can see the data that they are.

594
00:29:34,000 --> 00:29:35,000
And then you can see the data that they are.

595
00:29:35,000 --> 00:29:36,000
And then you can see the data that they are.

596
00:29:36,000 --> 00:29:37,000
And then you can see the data that they are.

597
00:29:37,000 --> 00:29:38,000
And then you can see the data that they are.

598
00:29:38,000 --> 00:29:39,000
And then you can see the data that they are.

599
00:29:39,000 --> 00:29:40,000
And then you can see the data that they are.

600
00:29:40,000 --> 00:29:41,000
And then you can see the data that they are.

601
00:29:41,000 --> 00:29:42,000
And then you can see the data that they are.

602
00:29:42,000 --> 00:29:43,000
And then you can see the data that they are.

603
00:29:43,000 --> 00:29:44,000
And then you can see the data that they are.

604
00:29:44,000 --> 00:29:45,000
And then you can see the data that they are.

605
00:29:45,000 --> 00:29:46,000
And then you can see the data that they are.

606
00:29:46,000 --> 00:29:47,000
And then you can see the data that they are.

607
00:29:47,000 --> 00:29:57,000
And then you can see the data that they are.

608
00:29:57,000 --> 00:29:58,000
And then you can see the data that they are.

609
00:29:58,000 --> 00:29:59,000
And then you can see the data that they are.

610
00:29:59,000 --> 00:30:00,000
And then you can see the data that they are.

611
00:30:00,000 --> 00:30:01,000
And then you can see the data that they are.

612
00:30:01,000 --> 00:30:02,000
And then you can see the data that they are.

613
00:30:02,000 --> 00:30:03,000
And then you can see the data that they are.

614
00:30:03,000 --> 00:30:04,000
And then you can see the data that they are.

615
00:30:04,000 --> 00:30:05,000
And then you can see the data that they are.

616
00:30:05,000 --> 00:30:06,000
And then you can see the data that they are.

617
00:30:06,000 --> 00:30:07,000
And then you can see the data that they are.

618
00:30:07,000 --> 00:30:08,000
And then you can see the data that they are.

619
00:30:08,000 --> 00:30:09,000
And then you can see the data that they are.

620
00:30:09,000 --> 00:30:10,000
And then you can see the data that they are.

621
00:30:10,000 --> 00:30:11,000
And then you can see the data that they are.

622
00:30:11,000 --> 00:30:12,000
And then you can see the data that they are.

623
00:30:12,000 --> 00:30:22,000
And then you can see the data that they are.

624
00:30:22,000 --> 00:30:23,000
And then you can see the data that they are.

625
00:30:23,000 --> 00:30:24,000
And then you can see the data that they are.

626
00:30:24,000 --> 00:30:25,000
And then you can see the data that they are.

627
00:30:25,000 --> 00:30:26,000
And then you can see the data that they are.

628
00:30:26,000 --> 00:30:27,000
And then you can see the data that they are.

629
00:30:27,000 --> 00:30:28,000
And then you can see the data that they are.

630
00:30:28,000 --> 00:30:29,000
And then you can see the data that they are.

631
00:30:29,000 --> 00:30:30,000
And then you can see the data that they are.

632
00:30:30,000 --> 00:30:31,000
And then you can see the data that they are.

633
00:30:31,000 --> 00:30:32,000
And then you can see the data that they are.

634
00:30:32,000 --> 00:30:33,000
And then you can see the data that they are.

635
00:30:33,000 --> 00:30:34,000
And then you can see the data that they are.

636
00:30:34,000 --> 00:30:35,000
And then you can see the data that they are.

637
00:30:35,000 --> 00:30:36,000
And then you can see the data that they are.

638
00:30:36,000 --> 00:30:37,000
And then you can see the data that they are.

639
00:30:37,000 --> 00:30:47,000
And then you can see the data that they are.

640
00:30:47,000 --> 00:30:49,000
And then you can see the data that they are.

641
00:30:49,000 --> 00:30:50,000
And then you can see the data that they are.

642
00:30:50,000 --> 00:30:51,000
And then you can see the data that they are.

643
00:30:51,000 --> 00:30:52,000
And then you can see the data that they are.

644
00:30:52,000 --> 00:30:53,000
And then you can see the data that they are.

645
00:30:53,000 --> 00:30:54,000
And then you can see the data that they are.

646
00:30:54,000 --> 00:30:55,000
And then you can see the data that they are.

647
00:30:55,000 --> 00:30:56,000
And then you can see the data that they are.

648
00:30:56,000 --> 00:30:57,000
And then you can see the data that they are.

649
00:30:57,000 --> 00:30:58,000
And then you can see the data that they are.

650
00:30:58,000 --> 00:30:59,000
And then you can see the data that they are.

651
00:30:59,000 --> 00:31:00,000
And then you can see the data that they are.

652
00:31:00,000 --> 00:31:01,000
And then you can see the data that they are.

653
00:31:01,000 --> 00:31:02,000
And then you can see the data that they are.

654
00:31:02,000 --> 00:31:03,000
And then you can see the data that they are.

655
00:31:03,000 --> 00:31:13,000
And then you can see the data that they are.

656
00:31:13,000 --> 00:31:14,000
And then you can see the data that they are.

657
00:31:14,000 --> 00:31:15,000
And then you can see the data that they are.

658
00:31:15,000 --> 00:31:16,000
And then you can see the data that they are.

659
00:31:16,000 --> 00:31:17,000
And then you can see the data that they are.

660
00:31:17,000 --> 00:31:18,000
And then you can see the data that they are.

661
00:31:18,000 --> 00:31:19,000
And then you can see the data that they are.

662
00:31:19,000 --> 00:31:20,000
And then you can see the data that they are.

663
00:31:20,000 --> 00:31:21,000
And then you can see the data that they are.

664
00:31:21,000 --> 00:31:22,000
And then you can see the data that they are.

665
00:31:22,000 --> 00:31:23,000
And then you can see the data that they are.

666
00:31:23,000 --> 00:31:24,000
And then you can see the data that they are.

667
00:31:24,000 --> 00:31:25,000
And then you can see the data that they are.

668
00:31:25,000 --> 00:31:26,000
And then you can see the data that they are.

669
00:31:26,000 --> 00:31:27,000
And then you can see the data that they are.

670
00:31:27,000 --> 00:31:28,000
And then you can see the data that they are.

671
00:31:28,000 --> 00:31:38,000
And then you can see the data that they are.

672
00:31:38,000 --> 00:31:39,000
And then you can see the data that they are.

673
00:31:39,000 --> 00:31:40,000
And then you can see the data that they are.

674
00:31:40,000 --> 00:31:41,000
And then you can see the data that they are.

675
00:31:41,000 --> 00:31:42,000
And then you can see the data that they are.

676
00:31:42,000 --> 00:31:43,000
And then you can see the data that they are.

677
00:31:43,000 --> 00:31:44,000
And then you can see the data that they are.

678
00:31:44,000 --> 00:31:45,000
And then you can see the data that they are.

679
00:31:45,000 --> 00:31:46,000
And then you can see the data that they are.

680
00:31:46,000 --> 00:31:47,000
And then you can see the data that they are.

681
00:31:47,000 --> 00:31:48,000
And then you can see the data that they are.

682
00:31:48,000 --> 00:31:49,000
And then you can see the data that they are.

683
00:31:49,000 --> 00:31:50,000
And then you can see the data that they are.

684
00:31:50,000 --> 00:31:51,000
And then you can see the data that they are.

685
00:31:51,000 --> 00:31:52,000
And then you can see the data that they are.

686
00:31:52,000 --> 00:31:53,000
And then you can see the data that they are.

687
00:31:53,000 --> 00:32:03,000
And then you can see the data that they are.

688
00:32:03,000 --> 00:32:04,000
And then you can see the data that they are.

689
00:32:04,000 --> 00:32:05,000
And then you can see the data that they are.

690
00:32:05,000 --> 00:32:06,000
And then you can see the data that they are.

691
00:32:06,000 --> 00:32:07,000
And then you can see the data that they are.

692
00:32:07,000 --> 00:32:08,000
And then you can see the data that they are.

693
00:32:08,000 --> 00:32:09,000
And then you can see the data that they are.

694
00:32:09,000 --> 00:32:10,000
And then you can see the data that they are.

695
00:32:10,000 --> 00:32:11,000
And then you can see the data that they are.

696
00:32:11,000 --> 00:32:12,000
And then you can see the data that they are.

697
00:32:12,000 --> 00:32:13,000
And then you can see the data that they are.

698
00:32:13,000 --> 00:32:14,000
And then you can see the data that they are.

699
00:32:14,000 --> 00:32:15,000
And then you can see the data that they are.

700
00:32:15,000 --> 00:32:16,000
And then you can see the data that they are.

701
00:32:16,000 --> 00:32:17,000
And then you can see the data that they are.

702
00:32:17,000 --> 00:32:18,000
And then you can see the data that they are.

703
00:32:18,000 --> 00:32:28,000
And then you can see the data that they are.

704
00:32:28,000 --> 00:32:29,000
And then you can see the data that they are.

705
00:32:29,000 --> 00:32:30,000
And then you can see the data that they are.

706
00:32:30,000 --> 00:32:31,000
And then you can see the data that they are.

707
00:32:31,000 --> 00:32:32,000
And then you can see the data that they are.

708
00:32:32,000 --> 00:32:33,000
And then you can see the data that they are.

709
00:32:33,000 --> 00:32:34,000
And then you can see the data that they are.

710
00:32:34,000 --> 00:32:35,000
And then you can see the data that they are.

711
00:32:35,000 --> 00:32:36,000
And then you can see the data that they are.

712
00:32:36,000 --> 00:32:37,000
And then you can see the data that they are.

713
00:32:37,000 --> 00:32:38,000
And then you can see the data that they are.

714
00:32:38,000 --> 00:32:39,000
And then you can see the data that they are.

715
00:32:39,000 --> 00:32:40,000
And then you can see the data that they are.

716
00:32:40,000 --> 00:32:41,000
And then you can see the data that they are.

717
00:32:41,000 --> 00:32:42,000
And then you can see the data that they are.

718
00:32:42,000 --> 00:32:43,000
And then you can see the data that they are.

719
00:32:43,000 --> 00:32:53,000
And then you can see the data that they are.

720
00:32:53,000 --> 00:32:54,000
And then you can see the data that they are.

721
00:32:54,000 --> 00:32:55,000
And then you can see the data that they are.

722
00:32:55,000 --> 00:32:56,000
And then you can see the data that they are.

723
00:32:56,000 --> 00:32:57,000
And then you can see the data that they are.

724
00:32:57,000 --> 00:32:58,000
And then you can see the data that they are.

725
00:32:58,000 --> 00:32:59,000
And then you can see the data that they are.

726
00:32:59,000 --> 00:33:00,000
And then you can see the data that they are.

727
00:33:00,000 --> 00:33:01,000
And then you can see the data that they are.

728
00:33:01,000 --> 00:33:02,000
And then you can see the data that they are.

729
00:33:02,000 --> 00:33:03,000
And then you can see the data that they are.

730
00:33:03,000 --> 00:33:04,000
And then you can see the data that they are.

731
00:33:04,000 --> 00:33:05,000
And then you can see the data that they are.

732
00:33:05,000 --> 00:33:06,000
And then you can see the data that they are.

733
00:33:06,000 --> 00:33:07,000
And then you can see the data that they are.

734
00:33:07,000 --> 00:33:08,000
And then you can see the data that they are.

735
00:33:08,000 --> 00:33:18,000
And then you can see the data that they are.

736
00:33:18,000 --> 00:33:19,000
And then you can see the data that they are.

737
00:33:19,000 --> 00:33:20,000
And then you can see the data that they are.

738
00:33:20,000 --> 00:33:21,000
And then you can see the data that they are.

739
00:33:21,000 --> 00:33:22,000
And then you can see the data that they are.

740
00:33:22,000 --> 00:33:23,000
And then you can see the data that they are.

741
00:33:23,000 --> 00:33:24,000
And then you can see the data that they are.

742
00:33:24,000 --> 00:33:25,000
And then you can see the data that they are.

743
00:33:25,000 --> 00:33:26,000
And then you can see the data that they are.

744
00:33:26,000 --> 00:33:27,000
And then you can see the data that they are.

745
00:33:27,000 --> 00:33:28,000
And then you can see the data that they are.

746
00:33:28,000 --> 00:33:29,000
And then you can see the data that they are.

747
00:33:29,000 --> 00:33:30,000
And then you can see the data that they are.

748
00:33:30,000 --> 00:33:31,000
And then you can see the data that they are.

749
00:33:31,000 --> 00:33:32,000
And then you can see the data that they are.

750
00:33:32,000 --> 00:33:33,000
And then you can see the data that they are.

751
00:33:33,000 --> 00:33:43,000
And then you can see the data that they are.

752
00:33:43,000 --> 00:33:44,000
And then you can see the data that they are.

753
00:33:44,000 --> 00:33:45,000
And then you can see the data that they are.

754
00:33:45,000 --> 00:33:46,000
And then you can see the data that they are.

755
00:33:46,000 --> 00:33:47,000
And then you can see the data that they are.

756
00:33:47,000 --> 00:33:48,000
And then you can see the data that they are.

757
00:33:48,000 --> 00:33:49,000
And then you can see the data that they are.

758
00:33:49,000 --> 00:33:50,000
And then you can see the data that they are.

759
00:33:50,000 --> 00:33:51,000
And then you can see the data that they are.

760
00:33:51,000 --> 00:33:52,000
And then you can see the data that they are.

761
00:33:52,000 --> 00:33:53,000
And then you can see the data that they are.

762
00:33:53,000 --> 00:33:54,000
And then you can see the data that they are.

763
00:33:54,000 --> 00:33:55,000
And then you can see the data that they are.

764
00:33:55,000 --> 00:33:56,000
And then you can see the data that they are.

765
00:33:56,000 --> 00:33:57,000
And then you can see the data that they are.

766
00:33:57,000 --> 00:33:58,000
And then you can see the data that they are.

767
00:33:58,000 --> 00:34:08,000
And then you can see the data that they are.

768
00:34:08,000 --> 00:34:09,000
And then you can see the data that they are.

769
00:34:09,000 --> 00:34:10,000
And then you can see the data that they are.

770
00:34:10,000 --> 00:34:11,000
And then you can see the data that they are.

771
00:34:11,000 --> 00:34:12,000
And then you can see the data that they are.

772
00:34:12,000 --> 00:34:13,000
And then you can see the data that they are.

773
00:34:13,000 --> 00:34:14,000
And then you can see the data that they are.

774
00:34:14,000 --> 00:34:15,000
And then you can see the data that they are.

775
00:34:15,000 --> 00:34:16,000
And then you can see the data that they are.

776
00:34:16,000 --> 00:34:17,000
And then you can see the data that they are.

777
00:34:17,000 --> 00:34:18,000
And then you can see the data that they are.

778
00:34:18,000 --> 00:34:19,000
And then you can see the data that they are.

779
00:34:19,000 --> 00:34:20,000
And then you can see the data that they are.

780
00:34:20,000 --> 00:34:21,000
And then you can see the data that they are.

781
00:34:21,000 --> 00:34:22,000
And then you can see the data that they are.

782
00:34:22,000 --> 00:34:23,000
And then you can see the data that they are.

783
00:34:23,000 --> 00:34:33,000
And then you can see the data that they are.

784
00:34:33,000 --> 00:34:35,000
And then you can see the data that they are.

785
00:34:35,000 --> 00:34:36,000
And then you can see the data that they are.

786
00:34:36,000 --> 00:34:37,000
And then you can see the data that they are.

787
00:34:37,000 --> 00:34:38,000
And then you can see the data that they are.

788
00:34:38,000 --> 00:34:39,000
And then you can see the data that they are.

789
00:34:39,000 --> 00:34:40,000
And then you can see the data that they are.

790
00:34:40,000 --> 00:34:41,000
And then you can see the data that they are.

791
00:34:41,000 --> 00:34:42,000
And then you can see the data that they are.

792
00:34:42,000 --> 00:34:43,000
And then you can see the data that they are.

793
00:34:43,000 --> 00:34:44,000
And then you can see the data that they are.

794
00:34:44,000 --> 00:34:45,000
And then you can see the data that they are.

795
00:34:45,000 --> 00:34:46,000
And then you can see the data that they are.

796
00:34:46,000 --> 00:34:47,000
And then you can see the data that they are.

797
00:34:47,000 --> 00:34:48,000
And then you can see the data that they are.

798
00:34:48,000 --> 00:34:49,000
And then you can see the data that they are.

799
00:34:49,000 --> 00:34:59,000
And then you can see the data that they are.

800
00:34:59,000 --> 00:35:00,000
And then you can see the data that they are.

801
00:35:00,000 --> 00:35:01,000
And then you can see the data that they are.

802
00:35:01,000 --> 00:35:02,000
And then you can see the data that they are.

803
00:35:02,000 --> 00:35:03,000
And then you can see the data that they are.

804
00:35:03,000 --> 00:35:04,000
And then you can see the data that they are.

805
00:35:04,000 --> 00:35:05,000
And then you can see the data that they are.

806
00:35:05,000 --> 00:35:06,000
And then you can see the data that they are.

807
00:35:06,000 --> 00:35:07,000
And then you can see the data that they are.

808
00:35:07,000 --> 00:35:08,000
And then you can see the data that they are.

809
00:35:08,000 --> 00:35:09,000
And then you can see the data that they are.

810
00:35:09,000 --> 00:35:10,000
And then you can see the data that they are.

811
00:35:10,000 --> 00:35:11,000
And then you can see the data that they are.

812
00:35:11,000 --> 00:35:12,000
And then you can see the data that they are.

813
00:35:12,000 --> 00:35:13,000
And then you can see the data that they are.

814
00:35:13,000 --> 00:35:14,000
And then you can see the data that they are.

815
00:35:14,000 --> 00:35:24,000
And then you can see the data that they are.

816
00:35:24,000 --> 00:35:25,000
And then you can see the data that they are.

817
00:35:25,000 --> 00:35:26,000
And then you can see the data that they are.

818
00:35:26,000 --> 00:35:27,000
And then you can see the data that they are.

819
00:35:27,000 --> 00:35:28,000
And then you can see the data that they are.

820
00:35:28,000 --> 00:35:29,000
And then you can see the data that they are.

821
00:35:29,000 --> 00:35:30,000
And then you can see the data that they are.

822
00:35:30,000 --> 00:35:31,000
And then you can see the data that they are.

823
00:35:31,000 --> 00:35:32,000
And then you can see the data that they are.

824
00:35:32,000 --> 00:35:33,000
And then you can see the data that they are.

825
00:35:33,000 --> 00:35:34,000
And then you can see the data that they are.

826
00:35:34,000 --> 00:35:35,000
And then you can see the data that they are.

827
00:35:35,000 --> 00:35:36,000
And then you can see the data that they are.

828
00:35:36,000 --> 00:35:37,000
And then you can see the data that they are.

829
00:35:37,000 --> 00:35:38,000
And then you can see the data that they are.

830
00:35:38,000 --> 00:35:39,000
And then you can see the data that they are.

831
00:35:39,000 --> 00:35:49,000
And then you can see the data that they are.

832
00:35:49,000 --> 00:35:50,000
And then you can see the data that they are.

833
00:35:50,000 --> 00:35:51,000
And then you can see the data that they are.

834
00:35:51,000 --> 00:35:52,000
And then you can see the data that they are.

835
00:35:52,000 --> 00:35:53,000
And then you can see the data that they are.

836
00:35:53,000 --> 00:35:54,000
And then you can see the data that they are.

837
00:35:54,000 --> 00:35:55,000
And then you can see the data that they are.

838
00:35:55,000 --> 00:35:56,000
And then you can see the data that they are.

839
00:35:56,000 --> 00:35:57,000
And then you can see the data that they are.

840
00:35:57,000 --> 00:35:58,000
And then you can see the data that they are.

841
00:35:58,000 --> 00:35:59,000
And then you can see the data that they are.

842
00:35:59,000 --> 00:36:00,000
And then you can see the data that they are.

843
00:36:00,000 --> 00:36:01,000
And then you can see the data that they are.

844
00:36:01,000 --> 00:36:02,000
And then you can see the data that they are.

845
00:36:02,000 --> 00:36:03,000
And then you can see the data that they are.

846
00:36:03,000 --> 00:36:04,000
And then you can see the data that they are.

847
00:36:04,000 --> 00:36:14,000
And then you can see the data that they are.

848
00:36:14,000 --> 00:36:15,000
And then you can see the data that they are.

849
00:36:15,000 --> 00:36:16,000
And then you can see the data that they are.

850
00:36:16,000 --> 00:36:17,000
And then you can see the data that they are.

851
00:36:17,000 --> 00:36:18,000
And then you can see the data that they are.

852
00:36:18,000 --> 00:36:19,000
And then you can see the data that they are.

853
00:36:19,000 --> 00:36:20,000
And then you can see the data that they are.

854
00:36:20,000 --> 00:36:21,000
And then you can see the data that they are.

855
00:36:21,000 --> 00:36:22,000
And then you can see the data that they are.

856
00:36:22,000 --> 00:36:23,000
And then you can see the data that they are.

857
00:36:23,000 --> 00:36:24,000
And then you can see the data that they are.

858
00:36:24,000 --> 00:36:25,000
And then you can see the data that they are.

859
00:36:25,000 --> 00:36:26,000
And then you can see the data that they are.

860
00:36:26,000 --> 00:36:27,000
And then you can see the data that they are.

861
00:36:27,000 --> 00:36:28,000
And then you can see the data that they are.

862
00:36:28,000 --> 00:36:29,000
And then you can see the data that they are.

863
00:36:29,000 --> 00:36:39,000
And then you can see the data that they are.

864
00:36:39,000 --> 00:36:41,000
And then you can see the data that they are.

865
00:36:41,000 --> 00:36:42,000
And then you can see the data that they are.

866
00:36:42,000 --> 00:36:43,000
And then you can see the data that they are.

867
00:36:43,000 --> 00:36:44,000
And then you can see the data that they are.

868
00:36:44,000 --> 00:36:45,000
And then you can see the data that they are.

869
00:36:45,000 --> 00:36:46,000
And then you can see the data that they are.

870
00:36:46,000 --> 00:36:47,000
And then you can see the data that they are.

871
00:36:47,000 --> 00:36:48,000
And then you can see the data that they are.

872
00:36:48,000 --> 00:36:49,000
And then you can see the data that they are.

873
00:36:49,000 --> 00:36:50,000
And then you can see the data that they are.

874
00:36:50,000 --> 00:36:51,000
And then you can see the data that they are.

875
00:36:51,000 --> 00:36:52,000
And then you can see the data that they are.

876
00:36:52,000 --> 00:36:53,000
And then you can see the data that they are.

877
00:36:53,000 --> 00:36:54,000
And then you can see the data that they are.

878
00:36:54,000 --> 00:36:55,000
And then you can see the data that they are.

879
00:36:55,000 --> 00:37:05,000
And then you can see the data that they are.

880
00:37:05,000 --> 00:37:06,000
And then you can see the data that they are.

881
00:37:06,000 --> 00:37:07,000
And then you can see the data that they are.

882
00:37:07,000 --> 00:37:08,000
And then you can see the data that they are.

883
00:37:08,000 --> 00:37:09,000
And then you can see the data that they are.

884
00:37:09,000 --> 00:37:10,000
And then you can see the data that they are.

885
00:37:10,000 --> 00:37:11,000
And then you can see the data that they are.

886
00:37:11,000 --> 00:37:12,000
And then you can see the data that they are.

887
00:37:12,000 --> 00:37:13,000
And then you can see the data that they are.

888
00:37:13,000 --> 00:37:14,000
And then you can see the data that they are.

889
00:37:14,000 --> 00:37:15,000
And then you can see the data that they are.

890
00:37:15,000 --> 00:37:16,000
And then you can see the data that they are.

891
00:37:16,000 --> 00:37:17,000
And then you can see the data that they are.

892
00:37:17,000 --> 00:37:18,000
And then you can see the data that they are.

893
00:37:18,000 --> 00:37:19,000
And then you can see the data that they are.

894
00:37:19,000 --> 00:37:20,000
And then you can see the data that they are.

895
00:37:20,000 --> 00:37:32,000
And then you can see the data that they are.

896
00:37:32,000 --> 00:37:34,000
And then you can see the data that they are.

897
00:37:34,000 --> 00:37:35,000
And then you can see the data that they are.

898
00:37:35,000 --> 00:37:36,000
And then you can see the data that they are.

899
00:37:36,000 --> 00:37:37,000
And then you can see the data that they are.

900
00:37:37,000 --> 00:37:38,000
And then you can see the data that they are.

901
00:37:38,000 --> 00:37:39,000
And then you can see the data that they are.

902
00:37:39,000 --> 00:37:40,000
And then you can see the data that they are.

903
00:37:40,000 --> 00:37:41,000
And then you can see the data that they are.

904
00:37:41,000 --> 00:37:42,000
And then you can see the data that they are.

905
00:37:42,000 --> 00:37:43,000
And then you can see the data that they are.

906
00:37:43,000 --> 00:37:44,000
And then you can see the data that they are.

907
00:37:44,000 --> 00:37:45,000
And then you can see the data that they are.

908
00:37:45,000 --> 00:37:46,000
And then you can see the data that they are.

909
00:37:46,000 --> 00:37:47,000
And then you can see the data that they are.

910
00:37:47,000 --> 00:37:48,000
And then you can see the data that they are.

911
00:37:48,000 --> 00:37:50,000
And then you can see the data that they are.

912
00:37:50,000 --> 00:37:51,000
And then you can see the data that they are.

913
00:37:51,000 --> 00:37:52,000
And then you can see the data that they are.

914
00:37:52,000 --> 00:37:53,000
And then you can see the data that they are.

915
00:37:53,000 --> 00:37:54,000
And then you can see the data that they are.

916
00:37:54,000 --> 00:37:55,000
And then you can see the data that they are.

917
00:37:55,000 --> 00:37:56,000
And then you can see the data that they are.

918
00:37:56,000 --> 00:37:57,000
And then you can see the data that they are.

919
00:37:57,000 --> 00:37:58,000
And then you can see the data that they are.

920
00:37:58,000 --> 00:37:59,000
And then you can see the data that they are.

921
00:37:59,000 --> 00:38:00,000
And then you can see the data that they are.

922
00:38:00,000 --> 00:38:01,000
And then you can see the data that they are.

923
00:38:01,000 --> 00:38:02,000
And then you can see the data that they are.

924
00:38:02,000 --> 00:38:03,000
And then you can see the data that they are.

925
00:38:03,000 --> 00:38:04,000
And then you can see the data that they are.

926
00:38:04,000 --> 00:38:05,000
And then you can see the data that they are.

927
00:38:05,000 --> 00:38:20,000
And then you can see the data that they are.

928
00:38:20,000 --> 00:38:21,000
And then you can see the data that they are.

929
00:38:21,000 --> 00:38:22,000
And then you can see the data that they are.

930
00:38:22,000 --> 00:38:23,000
And then you can see the data that they are.

931
00:38:23,000 --> 00:38:24,000
And then you can see the data that they are.

932
00:38:24,000 --> 00:38:25,000
And then you can see the data that they are.

933
00:38:25,000 --> 00:38:26,000
And then you can see the data that they are.

934
00:38:26,000 --> 00:38:27,000
And then you can see the data that they are.

935
00:38:27,000 --> 00:38:28,000
And then you can see the data that they are.

936
00:38:28,000 --> 00:38:29,000
And then you can see the data that they are.

937
00:38:29,000 --> 00:38:30,000
And then you can see the data that they are.

938
00:38:30,000 --> 00:38:31,000
And then you can see the data that they are.

939
00:38:31,000 --> 00:38:32,000
And then you can see the data that they are.

940
00:38:32,000 --> 00:38:33,000
And then you can see the data that they are.

941
00:38:33,000 --> 00:38:34,000
And then you can see the data that they are.

942
00:38:34,000 --> 00:38:35,000
And then you can see the data that they are.

943
00:38:35,000 --> 00:38:36,000
And then you can see the data that they are.

944
00:38:36,000 --> 00:38:37,000
And then you can see the data that they are.

945
00:38:37,000 --> 00:38:38,000
And then you can see the data that they are.

946
00:38:38,000 --> 00:38:39,000
And then you can see the data that they are.

947
00:38:39,000 --> 00:38:40,000
And then you can see the data that they are.

948
00:38:40,000 --> 00:38:41,000
And then you can see the data that they are.

949
00:38:41,000 --> 00:38:42,000
And then you can see the data that they are.

950
00:38:42,000 --> 00:38:43,000
And then you can see the data that they are.

951
00:38:43,000 --> 00:38:44,000
And then you can see the data that they are.

952
00:38:44,000 --> 00:38:45,000
And then you can see the data that they are.

953
00:38:45,000 --> 00:38:46,000
And then you can see the data that they are.

954
00:38:46,000 --> 00:38:47,000
And then you can see the data that they are.

955
00:38:47,000 --> 00:38:48,000
And then you can see the data that they are.

956
00:38:48,000 --> 00:38:49,000
And then you can see the data that they are.

957
00:38:49,000 --> 00:38:50,000
And then you can see the data that they are.

958
00:38:50,000 --> 00:39:05,000
And then you can see the data that they are.

959
00:39:05,000 --> 00:39:06,000
And then you can see the data that they are.

960
00:39:06,000 --> 00:39:07,000
And then you can see the data that they are.

961
00:39:07,000 --> 00:39:08,000
And then you can see the data that they are.

962
00:39:08,000 --> 00:39:09,000
And then you can see the data that they are.

963
00:39:09,000 --> 00:39:10,000
And then you can see the data that they are.

964
00:39:10,000 --> 00:39:11,000
And then you can see the data that they are.

965
00:39:11,000 --> 00:39:12,000
And then you can see the data that they are.

966
00:39:12,000 --> 00:39:13,000
And then you can see the data that they are.

967
00:39:13,000 --> 00:39:14,000
And then you can see the data that they are.

968
00:39:14,000 --> 00:39:15,000
And then you can see the data that they are.

969
00:39:15,000 --> 00:39:16,000
And then you can see the data that they are.

970
00:39:16,000 --> 00:39:17,000
And then you can see the data that they are.

971
00:39:17,000 --> 00:39:18,000
And then you can see the data that they are.

972
00:39:18,000 --> 00:39:19,000
And then you can see the data that they are.

973
00:39:19,000 --> 00:39:19,000
And then you can see the data that they are.

974
00:39:19,000 --> 00:39:21,000
And then you can see the data that they are.

975
00:39:21,000 --> 00:39:22,000
And then you can see the data that they are.

976
00:39:22,000 --> 00:39:23,000
And then you can see the data that they are.

977
00:39:23,000 --> 00:39:25,000
And then you can see the data that they are.

978
00:39:25,000 --> 00:39:26,000
And then you can see the data that they are.

979
00:39:26,000 --> 00:39:27,000
And then you can see the data that they are.

980
00:39:27,000 --> 00:39:28,000
And then you can see the data that they are.

981
00:39:28,000 --> 00:39:29,000
And then you can see the data that they are.

982
00:39:29,000 --> 00:39:30,000
And then you can see the data that they are.

983
00:39:30,000 --> 00:39:31,000
And then you can see the data that they are.

984
00:39:31,000 --> 00:39:32,000
And then you can see the data that they are.

985
00:39:32,000 --> 00:39:33,000
And then you can see the data that they are.

986
00:39:33,000 --> 00:39:34,000
And then you can see the data that they are.

987
00:39:34,000 --> 00:39:35,000
And then you can see the data that they are.

988
00:39:35,000 --> 00:39:36,000
And then you can see the data that they are.

989
00:39:36,000 --> 00:39:37,000
And then you can see the data that they are.

990
00:39:37,000 --> 00:39:47,000
And then you can see the data that they are.

991
00:39:47,000 --> 00:39:49,000
And then you can see the data that they are.

992
00:39:49,000 --> 00:39:50,000
And then you can see the data that they are.

993
00:39:50,000 --> 00:39:51,000
And then you can see the data that they are.

994
00:39:51,000 --> 00:39:52,000
And then you can see the data that they are.

995
00:39:52,000 --> 00:39:53,000
And then you can see the data that they are.

996
00:39:53,000 --> 00:39:54,000
And then you can see the data that they are.

997
00:39:54,000 --> 00:39:55,000
And then you can see the data that they are.

998
00:39:55,000 --> 00:39:56,000
And then you can see the data that they are.

999
00:39:56,000 --> 00:39:57,000
And then you can see the data that they are.

1000
00:39:57,000 --> 00:39:58,000
And then you can see the data that they are.

1001
00:39:58,000 --> 00:39:59,000
And then you can see the data that they are.

1002
00:39:59,000 --> 00:40:00,000
And then you can see the data that they are.

1003
00:40:00,000 --> 00:40:01,000
And then you can see the data that they are.

1004
00:40:01,000 --> 00:40:02,000
And then you can see the data that they are.

1005
00:40:02,000 --> 00:40:03,000
And then you can see the data that they are.

1006
00:40:03,000 --> 00:40:13,000
And then you can see the data that they are.

1007
00:40:13,000 --> 00:40:14,000
And then you can see the data that they are.

1008
00:40:14,000 --> 00:40:15,000
And then you can see the data that they are.

1009
00:40:15,000 --> 00:40:16,000
And then you can see the data that they are.

1010
00:40:16,000 --> 00:40:17,000
And then you can see the data that they are.

1011
00:40:17,000 --> 00:40:18,000
And then you can see the data that they are.

1012
00:40:18,000 --> 00:40:19,000
And then you can see the data that they are.

1013
00:40:19,000 --> 00:40:20,000
And then you can see the data that they are.

1014
00:40:20,000 --> 00:40:21,000
And then you can see the data that they are.

1015
00:40:21,000 --> 00:40:22,000
And then you can see the data that they are.

1016
00:40:22,000 --> 00:40:23,000
And then you can see the data that they are.

1017
00:40:23,000 --> 00:40:24,000
And then you can see the data that they are.

1018
00:40:24,000 --> 00:40:25,000
And then you can see the data that they are.

1019
00:40:25,000 --> 00:40:26,000
And then you can see the data that they are.

1020
00:40:26,000 --> 00:40:27,000
And then you can see the data that they are.

1021
00:40:27,000 --> 00:40:28,000
And then you can see the data that they are.

1022
00:40:28,000 --> 00:40:38,000
And then you can see the data that they are.

1023
00:40:38,000 --> 00:40:39,000
And then you can see the data that they are.

1024
00:40:39,000 --> 00:40:40,000
And then you can see the data that they are.

1025
00:40:40,000 --> 00:40:41,000
And then you can see the data that they are.

1026
00:40:41,000 --> 00:40:42,000
And then you can see the data that they are.

1027
00:40:42,000 --> 00:40:43,000
And then you can see the data that they are.

1028
00:40:43,000 --> 00:40:44,000
And then you can see the data that they are.

1029
00:40:44,000 --> 00:40:45,000
And then you can see the data that they are.

1030
00:40:45,000 --> 00:40:46,000
And then you can see the data that they are.

1031
00:40:46,000 --> 00:40:47,000
And then you can see the data that they are.

1032
00:40:47,000 --> 00:40:48,000
And then you can see the data that they are.

1033
00:40:48,000 --> 00:40:49,000
And then you can see the data that they are.

1034
00:40:49,000 --> 00:40:50,000
And then you can see the data that they are.

1035
00:40:50,000 --> 00:40:51,000
And then you can see the data that they are.

1036
00:40:51,000 --> 00:40:52,000
And then you can see the data that they are.

1037
00:40:52,000 --> 00:40:53,000
And then you can see the data that they are.

1038
00:40:53,000 --> 00:41:03,000
And then you can see the data that they are.

1039
00:41:03,000 --> 00:41:04,000
And then you can see the data that they are.

1040
00:41:04,000 --> 00:41:05,000
And then you can see the data that they are.

1041
00:41:05,000 --> 00:41:06,000
And then you can see the data that they are.

1042
00:41:06,000 --> 00:41:07,000
And then you can see the data that they are.

1043
00:41:07,000 --> 00:41:08,000
And then you can see the data that they are.

1044
00:41:08,000 --> 00:41:09,000
And then you can see the data that they are.

1045
00:41:09,000 --> 00:41:10,000
And then you can see the data that they are.

1046
00:41:10,000 --> 00:41:11,000
And then you can see the data that they are.

1047
00:41:11,000 --> 00:41:12,000
And then you can see the data that they are.

1048
00:41:12,000 --> 00:41:13,000
And then you can see the data that they are.

1049
00:41:13,000 --> 00:41:14,000
And then you can see the data that they are.

1050
00:41:14,000 --> 00:41:15,000
And then you can see the data that they are.

1051
00:41:15,000 --> 00:41:16,000
And then you can see the data that they are.

1052
00:41:16,000 --> 00:41:17,000
And then you can see the data that they are.

1053
00:41:17,000 --> 00:41:18,000
And then you can see the data that they are.

1054
00:41:18,000 --> 00:41:31,000
And yeah, I think, uh, actually, yeah, uh, Microsoft brings a lot of AI staff, especially co-pilot to all their products.

1055
00:41:31,000 --> 00:41:36,000
Yes, how is AI changing business intelligence today?

1056
00:41:36,000 --> 00:41:37,000
Yes, Miko.

1057
00:41:37,000 --> 00:41:41,000
So what is happening is, for example, now, what are the report, I published it, right?

1058
00:41:41,000 --> 00:41:47,000
So once I publish into workspace, now the end users as the capability to make the change.

1059
00:41:47,000 --> 00:41:49,000
And they can able to book market.

1060
00:41:49,000 --> 00:41:57,000
So whatever the published, which I, which I published the report into workspace, that it will not change.

1061
00:41:57,000 --> 00:42:02,000
But whatever the prompts they are giving and for example, if we have, for example, one bar chart.

1062
00:42:02,000 --> 00:42:06,000
So that if they give a prompt to change it to stagger call and chart, it will change.

1063
00:42:06,000 --> 00:42:14,000
And it will give the insight what they're looking and they, where they can also be a bookmark, they complete report.

1064
00:42:14,000 --> 00:42:25,000
So that's how I can see, uh, it was more into now, uh, where end users can able to change and whatever they look and feel they want to change and whatever the visuals they want to change.

1065
00:42:25,000 --> 00:42:27,000
So they can able to change and they can able to save.

1066
00:42:27,000 --> 00:42:35,000
So that's where they're getting more scope where people end users can able to look in for more business insights.

1067
00:42:35,000 --> 00:42:43,000
Yeah, I think for two years, Microsoft, uh, I have seen, I don't know, a demo and they, they show.

1068
00:42:43,000 --> 00:42:50,000
How they do, or how they automate ETL pipelines with AI.

1069
00:42:50,000 --> 00:42:54,000
I tried the same way, but for my company, it's not working.

1070
00:42:54,000 --> 00:43:02,000
It is, it is, I really, really helping data engineers or is it more a gimmick?

1071
00:43:02,000 --> 00:43:09,000
Yeah, so it would be, uh, I don't, basically we need to be fundamentals good with the fundamentals.

1072
00:43:09,000 --> 00:43:16,000
So as we see the genie in the data bricks, so it's a good, good thing that we, where we can able to analyze.

1073
00:43:16,000 --> 00:43:22,000
So I would suggest to utilize the genie for analysis purpose rather than, uh, for the coding.

1074
00:43:22,000 --> 00:43:28,000
So as the coding, if we have, as in when we have the fundamentals, so definitely we cannot be replaceable.

1075
00:43:28,000 --> 00:43:38,000
So that's where I would suggest for the developers, like where we should always be good with the fundamentals and strong with the fundamentals, whatever the tool, whatever the, uh, platform it is.

1076
00:43:38,000 --> 00:43:41,000
Definitely, we will not be replaced.

1077
00:43:41,000 --> 00:43:43,000
Good.

1078
00:43:43,000 --> 00:43:46,000
Um, uh, yeah.

1079
00:43:46,000 --> 00:43:57,000
Have you or what did you think can AI now ride the most of the backscode or how good is AI in ducks actually?

1080
00:43:57,000 --> 00:44:02,000
Yeah, sometimes if the problems which we are giving is not correct, yes, definitely.

1081
00:44:02,000 --> 00:44:08,000
But basically we should be aware of what the context and what the requirement we are looking for.

1082
00:44:08,000 --> 00:44:15,000
So definitely it will make a mistakes so that we should be aware of fundamentally what exactly we are looking for.

1083
00:44:15,000 --> 00:44:31,000
So certain level we can go for utilizing the help from the, uh, the A tools that we have, but definitely it's always our responsibility that we should ensure that we should be having a good perspective towards understanding of the certain functionalities.

1084
00:44:31,000 --> 00:44:34,000
Yeah, yeah.

1085
00:44:34,000 --> 00:44:45,000
What did you think in the age of AI, what should data professionals do to stay relevant actual?

1086
00:44:45,000 --> 00:44:56,000
So I would suggest not different completely on the A tools for your learning when you are learning or even your practicing, but utilize it for your analysis.

1087
00:44:56,000 --> 00:45:02,000
For if you are not able to understand the data of columns, then I would suggest to understand ask the prompt to understand.

1088
00:45:02,000 --> 00:45:07,000
So where what the data is happening, what the data is, how should I understand?

1089
00:45:07,000 --> 00:45:20,000
So if you can ask these type of questions, then you will be coming to conclusion that what the data is, how the data is, what as a reporting developer, what I can bring the insights to the table.

1090
00:45:20,000 --> 00:45:35,000
So as a best practice, I would suggest instead of going to utilize for the rest of the calculations, but I would suggest to understand the data point of view so that you will be the more better.

1091
00:45:35,000 --> 00:45:40,000
Come up with a story detailing from the report.

1092
00:45:40,000 --> 00:45:46,000
Yeah, and yeah, you have of this experience, this awesome experience.

1093
00:45:46,000 --> 00:45:56,000
And you work for Zeman Skensa, I work for Zeman Sashmi, as long as you.

1094
00:45:56,000 --> 00:46:06,000
And you also work for Royce Royce, I hope they give you the company car.

1095
00:46:06,000 --> 00:46:15,000
It's one of the kind, it's one of us lines, so yeah, it's a good, good, good to work with projects. Yeah, it's a great experience.

1096
00:46:15,000 --> 00:46:22,000
I think then we get the company car, you have to say, excellent.

1097
00:46:22,000 --> 00:46:26,000
But yeah, you work for all projects, these big great companies.

1098
00:46:26,000 --> 00:46:38,000
What have you learned on this project, what is the difference to, yeah, this enterprise companies to small projects.

1099
00:46:38,000 --> 00:46:48,000
Yeah, so each domain is different. So Zeman Skamesa is working into manufacturing and roles for us into.

1100
00:46:48,000 --> 00:46:57,000
And we have a craft, a craft domain engines and jazz at one into oil and gas. We into upstream downstream where we have.

1101
00:46:57,000 --> 00:47:00,000
So it's a good, good.

1102
00:47:00,000 --> 00:47:09,000
What I can say is very good experience, which I got from the data perspective and the business managers, what they are looking from their perspective.

1103
00:47:09,000 --> 00:47:20,000
And it is great to learn the many lessons from the, especially I feel from the data point of view, because that is the biggest asset we have for every reporting developer.

1104
00:47:20,000 --> 00:47:34,000
So I would suggest if you get a chance and opportunity in any of these sectors, definitely it would be a great asset for us to understand how their process flows or how the portfolios are.

1105
00:47:34,000 --> 00:47:44,000
So definitely it's a great to work with all these kind of best, which you got a chance to work with these clients.

1106
00:47:44,000 --> 00:47:53,000
And have you won project, you can tell a little bit, you're really proud of.

1107
00:47:53,000 --> 00:48:02,000
Yeah, so when I'm working with a craft domain, so where I got a chance to work with, so where customers are looking for.

1108
00:48:02,000 --> 00:48:06,000
Like where they are exactly looking for how.

1109
00:48:06,000 --> 00:48:15,000
What kind of invoice it is basically more of their report into invoice where it got paid and how many are due.

1110
00:48:15,000 --> 00:48:19,000
So they are focusing mainly on old use. So there are many more use.

1111
00:48:19,000 --> 00:48:25,000
So very less minute compared to the paid ones. So that's where I found it's very interesting report.

1112
00:48:25,000 --> 00:48:39,000
So there are certain challenges where we are unable to come up with a certain using key, that's functional is like, for example, if I take where we are trying to showcase to cumulative sales on a month on month for last 12 months from today.

1113
00:48:39,000 --> 00:48:41,000
So that's where it's a big challenging.

1114
00:48:41,000 --> 00:48:45,000
So at that moment, where the function called is on our.

1115
00:48:45,000 --> 00:48:51,000
There is a fund that's on called is on.

1116
00:48:51,000 --> 00:48:58,000
Is in scope, that's function exactly match it for the requirement, the values that we are looking for.

1117
00:48:58,000 --> 00:49:05,000
So that's where I found very interesting like took more time. So which that's function which that's function when we are looking for.

1118
00:49:05,000 --> 00:49:12,000
So that's where it's quite challenging and with respect to the customers as well, they're looking for.

1119
00:49:12,000 --> 00:49:20,000
The inside like where it gives more over use. So which aircrafts which company of aircrafts are giving more over use.

1120
00:49:20,000 --> 00:49:28,000
So that's where they are looking very excited for the from the report level. So that's where I felt very interesting report.

1121
00:49:28,000 --> 00:49:40,000
And yeah, we have, I think when we build reports and doing the data data engineer stuff, we have the one part is it's the development parts.

1122
00:49:40,000 --> 00:49:52,000
The other part is the designing part from the dashboards, but there is another I think for my perspective, the hardest part, it's the stakeholder management.

1123
00:49:52,000 --> 00:49:56,000
How do you handle the stakeholder requirements from your perspective?

1124
00:49:56,000 --> 00:49:57,000
Yeah, me.

1125
00:49:57,000 --> 00:50:02,000
Yeah, me. So we need to understand their requirements. We need to connect on a daily connects.

1126
00:50:02,000 --> 00:50:06,000
We need to ask for the feedback. So we need to check how.

1127
00:50:06,000 --> 00:50:13,000
What kind of is the insights they are getting from the report or not? So we need to better understand from their conversations.

1128
00:50:13,000 --> 00:50:21,000
And we should, if we can able to come up by having certain conversations, we can able to understand like what they're looking actually for.

1129
00:50:21,000 --> 00:50:27,000
So what kind of objectives they are looking forward to showcase to their leadership teams.

1130
00:50:27,000 --> 00:50:34,000
So that's how we need to ensure that we are coming up with the right insights.

1131
00:50:34,000 --> 00:50:39,000
So that's where I feel it's good to have the discussions with the stakeholders on a frequent connects.

1132
00:50:39,000 --> 00:50:44,000
So that's where it gives us good perspective, understand what exactly they are looking for.

1133
00:50:44,000 --> 00:50:55,000
And yeah, for beginners, what team tips can you give? We have all these Microsoft certifications on the Microsoft learn.

1134
00:50:55,000 --> 00:51:02,000
You have PL 300 and the DPS 6. How important do you think are these certificates actually?

1135
00:51:02,000 --> 00:51:09,000
Yes, me. So if you ask me like most companies are preferring for who are having the certifications, right.

1136
00:51:09,000 --> 00:51:17,000
So we will be thinking that it might not be much interesting or good to take, but most of the learners.

1137
00:51:17,000 --> 00:51:23,000
But I would suggest to take the exams and try to initially go with the Microsoft learn.

1138
00:51:23,000 --> 00:51:30,000
So where, for example, if you want to take PL 300 exam, I would suggest to go and type in the Google PL 300 Microsoft learn.

1139
00:51:30,000 --> 00:51:34,000
So where you'll find a page and where you'll be having certain modules.

1140
00:51:34,000 --> 00:51:40,000
So where there will be having four modules in each module, a topic wise, you will be having learning paths.

1141
00:51:40,000 --> 00:51:48,000
So if you can complete the learning path, then you can able to start going on to the power be application tool and you start practicing the hands on.

1142
00:51:48,000 --> 00:51:53,000
So that's where you can able to answer the when you go for the PL 300 exam.

1143
00:51:53,000 --> 00:52:03,000
So also, you would suggest to join the data days. So currently, fabric data is running and to be ending by August 6 in upcoming session in upcoming future.

1144
00:52:03,000 --> 00:52:14,000
If you can join the Microsoft's fabric data days or whatever the sessions they are conducting so that you can able to avail the free authors, you can register.

1145
00:52:14,000 --> 00:52:20,000
So where you can get a chance to get the free or just and you can start up playing for the exam.

1146
00:52:20,000 --> 00:52:24,000
So that's where I would suggest to start.

1147
00:52:24,000 --> 00:52:30,000
And I see on your profile on LinkedIn, you are a fabric community super user.

1148
00:52:30,000 --> 00:52:34,000
What is this for the eye?

1149
00:52:34,000 --> 00:52:45,000
Yeah, so basically this recognition I got where I used to provide the solutions to the questions that are getting posted around the world in the fabric community.

1150
00:52:45,000 --> 00:52:51,000
So it can be related to desktop or be disturbed. It can be related to power be, it can be related to power be for query.

1151
00:52:51,000 --> 00:52:55,000
It can be related to power be workspace service level.

1152
00:52:55,000 --> 00:53:00,000
So where I used to start answering the questions that are whichever I'm aware of.

1153
00:53:00,000 --> 00:53:08,000
So there are certain kind of where following they will be having like where we need to get certain solutions has accepted.

1154
00:53:08,000 --> 00:53:14,000
So that's where it brings us the badge of a big super user.

1155
00:53:14,000 --> 00:53:19,000
And okay, let's think.

1156
00:53:19,000 --> 00:53:24,000
I'm such a little seed is and it says awesome.

1157
00:53:24,000 --> 00:53:39,000
When it comes to you and say, hey, you get all all the money and, and yeah, all the power of people and so on developer, you will have what feature will you develop?

1158
00:53:39,000 --> 00:53:41,000
What feature?

1159
00:53:41,000 --> 00:53:46,000
I did not get your question, can you repeat what?

1160
00:53:46,000 --> 00:53:53,000
Yeah, when you can say to Microsoft, they say, give you all all the resources you need.

1161
00:53:53,000 --> 00:53:59,000
What feature for for a break or call be I will you develop?

1162
00:53:59,000 --> 00:54:03,000
Okay, feature for my side, I would.

1163
00:54:03,000 --> 00:54:07,000
I never started off thinking of this idea.

1164
00:54:07,000 --> 00:54:15,000
I think you're on this part, Mirko.

1165
00:54:15,000 --> 00:54:21,000
Yeah, I have an all all sessions, a lightning or rapid fire around.

1166
00:54:21,000 --> 00:54:25,000
I asked questions and you give a give a short answer.

1167
00:54:25,000 --> 00:54:32,000
So coffee tea or energy drink during development energy drink.

1168
00:54:32,000 --> 00:54:39,000
Dark mode or light mode. Sorry, dark mode or light mode light mode.

1169
00:54:39,000 --> 00:54:42,000
Favorite power be I feature.

1170
00:54:42,000 --> 00:54:46,000
A brick for be a feature.

1171
00:54:46,000 --> 00:54:49,000
One one any of any of the future.

1172
00:54:49,000 --> 00:54:51,000
What's the your your favorite?

1173
00:54:51,000 --> 00:54:52,000
Yeah, data.

1174
00:54:52,000 --> 00:54:53,000
Legend.

1175
00:54:53,000 --> 00:54:57,000
Okay, most underrated Microsoft technology.

1176
00:54:57,000 --> 00:55:01,000
I don't have anything in my mind.

1177
00:55:01,000 --> 00:55:06,000
One, everyone should know one one.

1178
00:55:06,000 --> 00:55:10,000
One that's function everyone should know about.

1179
00:55:10,000 --> 00:55:11,000
Calculate.

1180
00:55:11,000 --> 00:55:18,000
But one feature you love Microsoft to add on on.

1181
00:55:18,000 --> 00:55:21,000
On power be I.

1182
00:55:21,000 --> 00:55:26,000
So as of now I'm finding in the data modeling, when I see the table view,

1183
00:55:26,000 --> 00:55:31,000
I'm going to like certain columns will be on the right side.

1184
00:55:31,000 --> 00:55:36,000
So if if if Microsoft can provide a functionality of dragging and placing reordering the columns.

1185
00:55:36,000 --> 00:55:39,000
So that's that that I have that in my mind.

1186
00:55:39,000 --> 00:55:40,000
Yeah.

1187
00:55:40,000 --> 00:55:47,000
Is there one book podcast YouTube channel every data professional should listen to.

1188
00:55:47,000 --> 00:55:50,000
I would say.

1189
00:55:50,000 --> 00:55:57,000
I did have many but right now I'm not getting into my mind.

1190
00:55:57,000 --> 00:56:02,000
What your favorite productivity had it.

1191
00:56:02,000 --> 00:56:11,000
So my third productivity habit is to daily you can spend some time on your learnings,

1192
00:56:11,000 --> 00:56:14,000
whichever you feel not.

1193
00:56:14,000 --> 00:56:17,000
More good in so that's where I feel.

1194
00:56:17,000 --> 00:56:23,000
To start spending some time on a daily basis so that would improve are definitely our.

1195
00:56:23,000 --> 00:56:26,000
Yeah.

1196
00:56:26,000 --> 00:56:35,000
So then my mind one of my closing question is what did you think power be I looks like in three years.

1197
00:56:35,000 --> 00:56:45,000
So definitely we can see many updates many many updates and also will be seeing how the fabric is getting a world from the current preview features to

1198
00:56:45,000 --> 00:56:50,000
generally available and working directly reporting in the fabric environment itself.

1199
00:56:50,000 --> 00:56:55,000
So that's where I want to see how the fabric is evolving.

1200
00:56:55,000 --> 00:57:09,000
Also, yeah, so then my last question is when when I should invite or the next guest who should be in what question should I ask them.

1201
00:57:09,000 --> 00:57:13,000
In the next podcast what questions you want to ask.

1202
00:57:13,000 --> 00:57:21,000
Yeah, I should how should I invite and what questions roll I ask it.

1203
00:57:21,000 --> 00:57:31,000
Yeah, so mostly you can ask on the fabric part I would suggest to go go with the fabric on kind of lake houses, where houses, so how how the data is.

1204
00:57:31,000 --> 00:57:41,000
How which kind of how the volume is how they what the real time intelligence and what kind of applications are currently organized are using so that's where you can go with completely.

1205
00:57:41,000 --> 00:57:48,000
And then actually in the fabric environment and have you an idea for the next guest.

1206
00:57:48,000 --> 00:57:52,000
I don't have I'm happy to hear a little bit.

1207
00:57:52,000 --> 00:58:05,000
Yeah, so yeah, this was awesome awesome session. Thank you for staying here with me these hour and yeah, thank you for joining the M65 podcast and sharing your experience.

1208
00:58:05,000 --> 00:58:13,000
Yeah, building scalable power by the I solutions Microsoft fabric enterprise analytics and yeah, joining all data into real business value.

1209
00:58:13,000 --> 00:58:19,000
So thank you for staying here with me and yeah, it was amazing sessions. Thank you.

1210
00:58:19,000 --> 00:58:23,000
Yeah, thank you. Thank you, Michael. Thank you for this. Thank you all for joining in.

1211
00:58:23,000 --> 00:58:25,000
Bye.

1212
00:58:25,000 --> 00:58:35,000
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1213
00:58:35,000 --> 00:58:45,000
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1214
00:58:45,000 --> 00:58:55,000
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