Microsoft Azure Data Engineer Associate (DP-203) Cert Prep: 2 Design and Develop Data Processing by Microsoft Press

Explore the fundamental concepts and skills required to design and develop data processing to pass the Microsoft Azure Data Engineer Associate (DP-203) certification exam.

1. Ingest and Transform Data

• Learning objectives
• Transform data by using Apache Spark
• Transform data by using Transact-SQL
• Transform data by using Data Factory
• Transform data by using Azure Synapse pipelines
• Transform data by using Stream Analytics

2. Work with Transformed Data

• Learning objectives
• Cleanse data
• Split data
• Shred JSON
• Encode and decode data

3. Troubleshoot Data Transformations

• Learning objectives
• Configure error handling for the transformation
• Normalize and denormalize values
• Transform data by using Scala
• Perform data exploratory analysis

4. Design a Batch Processing Solution

• Learning objectives
• Develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse pipelines, PolyBase, and Azure Databricks
• Create data pipelines
• Design and implement incremental data loads
• Design and develop slowly changing dimensions
• Handle security and compliance requirements
• Scale resources

5. Develop a Batch Processing Solution

• Learning objectives
• Configure the batch size
• Design and create tests for data pipelines
• Integrate Jupyter and Python Notebooks into a data pipeline
• Handle duplicate data
• Handle missing data
• Handle late-arriving data

6. Configure a Batch Processing Solution

• Learning objectives
• Upsert data
• Regress to a previous state
• Design and configure exception handling
• Configure batch retention
• Revisit batch processing solution design
• Debug Spark jobs by using the Spark UI

7. Design a Stream Processing Solution

• Learning objective
• Develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
• Process data by using Spark structured streaming
• Monitor for performance and functional regressions
• Design and create windowed aggregates
• Handle schema drift

8. Process Data in a Stream Processing Solution

• Learning objectives
• Process time series data
• Process across partitions
• Process within one partition
• Configure checkpoints and watermarking during processing
• Scale resources
• Design and create tests for data pipelines
• Optimize pipelines for analytical or transactional purposes

9. Troubleshoot a Stream Processing Solution

• Learning objectives
• Handle interruptions
• Design and configure exception handling
• Upsert data
• Replay archived stream data
• Design a stream processing solution

10. Manage Batches and Pipelines

• Learning objectives
• Trigger batches
• Handle failed batch loads
• Validate batch loads
• Manage data pipelines in Data Factory and Synapse pipelines
• Schedule data pipelines in Data Factory and Synapse pipelines
• Implement version control for pipeline artifacts
• Manage Spark jobs in a pipeline