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DP-203 Data Engineering on Microsoft Azure

Master the entire Azure analytics stack – from lake to warehouse to real-time stream – in 4 instructor-led days.

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Why choose this course?

  • Full breadth of Azure data services. You’ll work with Synapse Analytics, Data Factory, Data Lake Gen2, Databricks, Stream Analytics, Event Hubs and more.
  • End-to-end project labs. Orchestrate ingestion, lake house transformation, warehouse loading, HTAP links and real-time dashboards in a single, story-driven scenario.
  • Designed for tomorrow. DP-203 underpins Fabric and AI workloads, so the skills stay relevant even as Microsoft retires DP-203 in favour of DP-700 (effective 31 Dec 2025).
  • Hybrid delivery – attend on-campus or virtually from anywhere in South Africa.
See Course Content

This course is ideal for:

  • Data engineers and architects building analytical solutions on Azure.
  • BI developers moving from on-prem SQL/SSIS to Synapse + ADF pipelines.
  • Data scientists and analysts who need production-grade data pipelines.
  • Anyone preparing for Exam DP-203 on their Microsoft certification journey.

Prerequisites

  • Working knowledge of cloud computing and basic data concepts.
  • Hands-on experience with at least one data-solution technology (SQL, Python, Spark, or ETL tooling) is recommended.

Course Content

  • Get started with data engineering on Azure – core roles and tasks, ADLS Gen2 fundamentals and an overview of Azure Synapse Analytics.
  • Build data-analytics solutions using Azure Synapse serverless SQL pools – query files in a data-lake with T-SQL, transform data and optimise cost/performance.
  • Perform data engineering with Azure Synapse Apache Spark Pools – DataFrames, Spark SQL, Delta Lake and advanced transformation patterns.
  • Transfer and transform data with Azure Synapse Analytics pipelines – copy, map-data-flow and notebook activities, triggers, monitoring and CI/CD.
  • Implement a data-analytics solution with Azure Synapse Analytics – combine serverless SQL, Spark and dedicated pools in a unified lake-house architecture.
  • Work with data warehouses using Azure Synapse Analytics – design, load and optimise dedicated SQL pools for enterprise BI workloads.
  • Work with Hybrid Transactional and Analytical Processing (HTAP) solutions using Azure Synapse Analytics – plan and implement Synapse Link for near real-time analytics on operational data.
  • Implement a data-streaming solution with Azure Stream Analytics – ingest from Event Hubs/IoT Hub, window queries, output to lake or warehouse and build real-time dashboards.
  • Implement a data-lakehouse analytics solution with Azure Databricks – Delta Lake tables, notebooks, structured streaming and Medallion architecture best practices.

Hardware Requirements

Interested?

Enquire today and one of our consultants will be in touch.