Azure Data Engineer (DP-203)

Understanding the Azure data platform and core services.

• Overview of the Azure data ecosystem (ADLS, ADF, Synapse, Databricks).
• Data warehouse vs. Data Lake vs. Lakehouse on Azure.
• Azure storage accounts (Blob Storage, Data Lake Storage Gen2).
• Provisioning Data Engineering resources.

Implementing the foundation of your data lake.

• ADLS Gen2 architecture and features.
• Hierarchical namespace and directory structure.
• Security: RBAC and Access Control Lists (ACLs).
• Data ingestion patterns into ADLS.

Building code-free ETL and ELT pipelines.

• ADF core components (Pipelines, Activities, Linked Services, Datasets).
• Building your first pipeline with the Copy Data activity.
• Integration Runtimes (Azure, Self-Hosted).
• Orchestration with triggers (Schedule, Tumbling Window, Event).

Transforming data using various compute engines.

• Mapping Data Flows for visual data transformation.
• Control flow activities (ForEach, If Condition, Stored Procedure).
• Executing Databricks notebooks and Spark jobs.
• Parameters and variables for dynamic pipelines.

Understanding the unified analytics platform.

• Synapse Analytics components (Workspace, SQL Pools, Spark Pools).
• Synapse Studio for unified development.
• Dedicated SQL Pool (Data Warehousing) architecture.
• Serverless SQL Pool for data lake exploration.

Building and optimizing a modern data warehouse.

• Designing table schemas (Distribution, Indexing).
• Loading data with PolyBase and the COPY command.
• Data warehousing best practices (Star Schema).
• Query optimization and workload management.

Using Apache Spark within the Synapse environment.

• Creating and managing Spark pools.
• Developing Spark notebooks (PySpark, Spark SQL).
• Reading and writing data from the data lake.
• Integrating Spark pools with SQL pools.

Leveraging the optimized Spark platform on Azure.

• Databricks architecture (Workspace, Clusters, Notebooks).
• Databricks File System (DBFS).
• Running Spark jobs and notebooks in Databricks.
• Orchestrating Databricks jobs with Azure Data Factory.

Bringing reliability to your data lake with Delta Lake.

• Delta Lake architecture and benefits (ACID, Time Travel).
• Converting Parquet to Delta format.
• Implementing UPSERTs (MERGE) and CDC.
• Building a multi-hop (Bronze/Silver/Gold) data architecture.

Processing streaming data on the fly.

• Azure Event Hubs for data ingestion.
• Azure Stream Analytics for real-time processing.
• Windowing functions in Stream Analytics Query Language (SAQL).
• Integrating Stream Analytics with Power BI and Data Lake.

Ensuring your data platform is secure and performant.

• Securing data at rest and in transit.
• Azure Key Vault for managing secrets.
• Monitoring pipelines with Azure Monitor and Data Factory Analytics.
• Implementing data governance with Azure Purview.

Final review and exam strategies.

• Review of the core exam domains (Storage, Processing, Security).
• Case study workshop: Designing an end-to-end solution.
• Full-Length Practice Exam Simulation.
• Review of Practice Exam & Final Q&A.