Azure Data Engineer DP-203 Foundations
A structured Microsoft DP-203 course covering storage, transformation, pipelines, security, monitoring, and Synapse, with linked practice questions.
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What you will learn
- Describe storage and ingestion.
- Explain transformation and pipelines.
- Describe security and governance.
- Explain monitoring and optimization.
- Describe Synapse and analytics.
Before you start
- Azure Data Fundamentals knowledge is helpful.
- Some SQL and Python experience is recommended.
- No previous Azure data certification is required.
Lesson 1 Storage and Ingestion
The Azure Data Engineer Associate DP-203 exam validates skills for designing and implementing data solutions. Azure Blob Storage and Azure Data Lake Storage store raw and processed files. Azure Event Hubs ingests streaming events, and Azure IoT Hub ingests device telemetry. Azure Data Factory orchestrates copy activities between sources and sinks. A data lake stores raw data in native formats, and ELT means extract, load, then transform. Partition keys distribute data for scale, and managed identities reduce credential handling. Data engineers design ingestion for batch and streaming workloads, choosing sources, destinations, and formats carefully.
Example
Example: IoT Hub collects sensor data, Event Hubs buffers streaming events, and Data Factory copies batched files into Azure Data Lake Storage.Lesson 2 Transformation and Pipelines
Data transformation turns raw data into analytics-ready data. Azure Databricks runs Apache Spark notebooks, Synapse SQL runs relational transformations, and mapping data flows provide visual ETL in Data Factory. Fact tables store measurable events, dimension tables store descriptive attributes, and window functions aggregate over related rows. Azure Data Factory pipelines contain activities, and triggers start runs on schedules or events. Linked services define connections, and datasets describe data structure. Checkpoints let streaming jobs resume, and idempotent designs allow safe reruns. Data engineers choose transformations based on volume, latency, and team skills.
Example
Example: A pipeline uses a mapping data flow to clean sales data, a notebook to add features, and Synapse SQL to load the star schema.Lesson 3 Security and Governance
Data security protects data at rest, in transit, and in use. Azure Key Vault stores secrets, keys, and certificates, while managed identities let services authenticate without stored credentials. Encryption at rest protects stored data, and TLS protects data in transit. Private endpoints keep traffic inside a virtual network, and dynamic data masking hides sensitive values in query results. Microsoft Purview catalogs assets, tracks lineage, and classifies data. Azure RBAC controls permissions, and audit logs record activity. Data engineers must apply least privilege, use private networking, and document governance so data remains safe and compliant.
Example
Example: A pipeline uses a managed identity, connects through a private endpoint, stores credentials in Key Vault, and masks sensitive columns in queries.Lesson 4 Monitoring and Optimization
Monitoring and optimization keep data solutions reliable and cost-effective. Azure Monitor collects metrics, logs, and alerts from pipelines and resources. Metrics are numeric measurements, logs record events, and alerts notify teams when conditions are met. Diagnostic settings route logs to Log Analytics, storage, or Event Hubs. Cost optimization uses budgets, recommendations, and storage tiers such as hot and cool. Query performance insights identify slow queries, and capacity planning sizes compute for demand. Data engineers should define service level objectives, monitor pipeline runs, and review cost reports regularly.
Example
Example: A data engineer creates alerts for failed pipeline runs, uses diagnostic settings to send logs to Log Analytics, and reviews a monthly budget report.Lesson 5 Synapse and Analytics
Azure Synapse Analytics unifies data warehousing, big data, and orchestration. A Synapse workspace contains SQL pools, Spark pools, pipelines, and linked data. Dedicated SQL pools provide provisioned warehouse compute, while serverless SQL pools query files on demand. Spark pools run large-scale Scala, Python, and SQL processing. Synapse pipelines orchestrate data movement, and lake databases define schemas over data lake files. Data flows provide visual transformations. Synapse connects to Data Lake Storage, Event Hubs, and many other sources, making it a central analytics platform. Data engineers design star schemas, partition data, and choose SQL or Spark based on workload.