You’ll learn how to revolutionize data prep and modeling using Fabric Notebooks to streamline workflows and solve common data challenges — directly inside the Microsoft cloud — in this episode.
Who this episode is for:
• You want practical strategies you can apply instantly
• You want real execution — not theory
• You want to unlock Microsoft 365, Power Platform, and Azure for real business outcomes
Scenario:
Wrangling messy data across tools like PowerBI, Excel, and legacy scripts takes hours and invites errors.
Step-by-step – what you will learn:
• How Fabric Notebooks centralize your data workflow
• How to script transformations directly in Python or R
• How to connect notebooks with Spark for scalable data processing
• How to integrate cleaned datasets into PowerBI dashboards or machine learning pipelines
Tools + tech included:
• Microsoft 365
• Fabric Notebooks
• Spark integration
• Lakehouse environments
Practical payoff:
• Reduce manual effort and errors
• Eliminate disconnected workflows
• Improve visibility and repeatability in data pipelines
Open topical anchors:
productivity improvement • data transformation strategy • cloud-first workflows • Microsoft ecosystem advantage
Example business cases listeners can apply immediately:
• Preparing sales data for real-time PowerBI dashboards
• Feature engineering for customer churn predictions
• Scaling workflows for millions of data rows without bottlenecks
Outcome statement:
By the end of this episode — you’ll be equipped to transform complex data workflows into streamlined, transparent systems using Fabric Notebooks.
Call-to-action:
Start building your skills today.
Transform your workflows now.
#datamodeling #machinelearning #powerbi #datapreparation #etlprocess
CHAPTERS:
00:00 - Intro
00:23 - Why Fabric Notebooks
04:59 - Raw Lakehouse Data to ML Preparation
10:09 - Lakehouse, Notebooks, and Spark Integration
15:49 - Collaboration, Governance, and Scaling Up
19:55 - Importance of Git Integration for Data Science
20:21 - Necessity of Code Review and Credential Checks
21:20 - Characteristics of Pro Data Science Teams
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