Data ingestion, transformation, batch processing, stream processing, and pipeline management using Azure Data Factory, Synapse Analytics, Databricks, and Stream Analytics.
The largest exam domain covering data ingestion and transformation using Apache Spark, T-SQL, Azure Synapse Pipelines, Azure Data Factory, and Azure Stream Analytics. Includes developing batch processing solutions with Azure Data Lake Storage Gen2, Azure Databricks, and Azure Synapse Analytics, as well as stream processing with Azure Event Hubs and Spark structured streaming. Also covers managing data pipelines, scheduling, version control, error handling, and Delta Lake operations. This domain represents 40–45% of the exam.
5 minutes
5 Questions
Develop Data Processing is a critical domain within the Azure Data Engineer Associate certification that focuses on designing, implementing, and managing data transformation and processing solutions using Azure services. This domain typically accounts for a significant portion of the exam and encompasses several key areas.
**Batch Processing:** Engineers must understand how to implement batch processing solutions using Azure Data Factory (ADF), Azure Synapse Analytics, and Azure Databricks. This includes creating pipelines, designing data flows, managing triggers, and orchestrating complex ETL/ELT workflows. Understanding how to handle incremental loads, full loads, and change data capture (CDC) is essential.
**Stream Processing:** Real-time data processing using Azure Stream Analytics, Azure Event Hubs, and Azure IoT Hub is a core component. Engineers should know how to configure windowing functions (tumbling, hopping, sliding, session), handle late-arriving data, manage watermarks, and design solutions for real-time analytics.
**Data Transformation:** This involves using technologies like Spark (via Databricks or Synapse Spark pools), T-SQL, and Data Flows in ADF/Synapse to cleanse, aggregate, merge, and reshape data. Engineers must be proficient in handling schema drift, data type conversions, and implementing complex business logic within transformation pipelines.
**Error Handling and Monitoring:** Implementing robust error handling, retry policies, logging, and monitoring using Azure Monitor, Log Analytics, and built-in pipeline monitoring tools ensures data pipeline reliability and observability.
**Performance Optimization:** Engineers should optimize data processing by implementing partitioning strategies, caching, parallelism, resource scaling, and choosing appropriate compute sizes. Understanding Spark optimization techniques like broadcast joins, partition pruning, and adaptive query execution is important.
**Upsert and Merge Patterns:** Implementing slowly changing dimensions (SCD), merge operations using Delta Lake, and managing data versioning are key skills.
Overall, Develop Data Processing requires engineers to build scalable, reliable, and efficient data pipelines that transform raw data into meaningful insights while ensuring data quality, governance, and cost-effectiveness across the Azure ecosystem.Develop Data Processing is a critical domain within the Azure Data Engineer Associate certification that focuses on designing, implementing, and managing data transformation and processing solutions using Azure services. This domain typically accounts for a significant portion of the exam and encom…
