Transform and Analyze Data with Microsoft Fabric
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Model star schemas, clean and join data with SQL and PySpark, and tune Delta Lake tables and capacity use in Microsoft Fabric through hands-on exercises.
Microsoft Fabric offers both lakehouses and warehouses for storing data, and this course works through how to shape data inside them so that analysis stays fast and efficient. You model tables as star or snowflake schemas, build fact and dimension tables, and handle slowly changing dimensions. You then clean, filter, aggregate and join data with SQL and PySpark, and use the Fabric Capacity Metrics app and Delta Lake table optimization to find and fix slow workloads.
What You Will Achieve
- Choose between a star and a snowflake schema for a given data modeling scenario
- Design dimension tables that keep history by applying Type 2 slowly changing dimension changes
- Clean lakehouse data, convert its types and combine tables using PySpark merges and SQL joins
- Track capacity and storage use in the Capacity Metrics app and run OPTIMIZE on Delta tables
Program Overview
Fabric Data Engineering and Data Modeling
Starts with a first lakehouse, compares lakehouse and warehouse items, and introduces the schema designs that suit different data modeling scenarios.
- Creating a first lakehouse
- Lakehouse versus warehouse storage
- Star and snowflake schemas
- Data modeling scenarios
Implementing Star Schemas and Dimensions
Covers building a star schema in a lakehouse or warehouse, including how dimension data changes over time and when to denormalize.
- Fact and dimension tables
- SCD types and Type 2 changes
- Date dimensions and bridge tables for multivalued dimensions
- Normalization versus denormalization
Data Transformation, Exploration, and Performance Optimization
Works through cleansing, aggregating and merging data, then monitors capacity and storage use and applies performance improvements to Dataflows and Delta Lake tables.
- Cleansing, type conversion and PySpark filtering
- Aggregation, SQL joins and PySpark merges
- Data profiling, XMLA and SQL endpoints
- Capacity Metrics app, Dataflows and the OPTIMIZE operation
Certificate Awarded (Paid)
Complete the course to earn it.
Job Outlook
These skills apply to data engineering, data science and business intelligence work on teams that store, model and query data in Microsoft Fabric lakehouses and warehouses.
Good for: Data analysts and engineers who want guided, hands-on practice modeling, transforming and tuning data inside a working Fabric environment.
Keep in mind: It covers part of the DP-600 topics, but the Fabric Analytics Engineer Associate certification is a separate Microsoft exam; the course itself gives a certificate of completion.
Bottom line: A practical run through Fabric star schema design, slowly changing dimensions and Delta Lake tuning, with exercises done in a provided Fabric account.
Frequently Asked Questions
Where do I complete the hands-on exercises?
The exercises run on the Fabric platform itself. A Fabric account is provided during the course, and each exercise gives step-by-step instructions to follow in that account.
Will this course prepare me for the DP-600 exam?
It lines up with part of the topics in Microsoft's Fabric Analytics Engineer Associate certification (DP-600), such as data transformation, modeling and performance optimization. The certification is a separate exam, and finishing the course earns a certificate of completion rather than the Microsoft credential.
Which languages are used to transform the data?
The transformation lessons use SQL and PySpark. You filter and merge data with PySpark, join tables with SQL, and look at use cases for the XMLA and SQL endpoints and how to choose a SQL query tool.
What does the course teach about slowly changing dimensions?
Within the star schema chapter, the lessons compare the types of slowly changing dimensions and then walk through implementing Type 2 changes, next to other special cases such as date dimensions and bridge tables.
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