DP-203 Ended in 2025, How To Become an Azure Data Engineer

Choose one workflow you can already describe, such as loading changed orders into a reporting table. Write down the source, target, transformation rules, failure risks and the person who uses the output. This converts past work into a project brief instead of starting with unfamiliar services.

Your brief should include one measurable change rule, such as a last_modified field. That is relevant because data engineers integrate and transform data while supporting reliable pipelines and stores, according to Microsoft’s data-engineer role description.

Use your sample data to write the joins, filters, aggregations and data-quality checks that the pipeline will need. Then recreate one transformation in Python so you can read data, inspect types and handle missing values before moving into Spark.

This is a prerequisite-focused step, not a reason to delay the project. Microsoft’s Azure Databricks data-engineering learning path lists familiarity with SQL and Python among its prerequisites. Read the learning-path requirements.

Create Azure Data Lake Storage and organise separate locations for source data and transformed output. Enable the hierarchical namespace when configuring the storage account, then upload a small source file or load a sample extract.

Azure Data Lake Storage is built on Blob Storage, and its hierarchical namespace enables the data-lake capabilities used for big-data analytics. Microsoft’s ADLS overview explains the storage model.

In Azure Data Factory, create the linked services, source and destination datasets, then build a pipeline that copies only new or updated records. Store the last processed watermark and run the pipeline once with an initial load, then add or update source rows and run it again.

Microsoft’s tutorial uses a watermark column to slice new or changed records and includes creating, running and monitoring the pipeline. Follow the incremental-copy pattern.

Use an Azure Databricks notebook to load the copied data, apply the SQL and Python transformations you prepared, and write a clean output. Check row counts, null handling and at least one aggregation against your expected result.

Apache Spark powers compute clusters and SQL warehouses in Azure Databricks. Microsoft’s Spark overview provides the platform context. In ADF, inspect the pipeline run and capture the status, activity details and any failure message you resolved.

Commit your pipeline definitions, notebook, sample-safe configuration and README to GitHub. In the README, include the architecture, watermark rule, validation result, run evidence and next production improvement. Azure Data Factory can integrate with GitHub for source control, collaboration and versioning. Microsoft’s ADF source-control guidance explains that integration.

Prepare interview answers around your own choices: why the source and target were selected, how the watermark prevents unnecessary copying, how you validated the transformation and what you would monitor in production. If you need a smaller scope first, use the project estimator.