Azure Databricks and Spark for Data Engineers — Ramesh Retnasamy

Retnasamy develops data-engineering workflows using Spark on Azure Databricks. PySpark, Spark SQL, Delta, catalog management, and scheduled jobs connect data transformation to an operational pipeline. The course is most useful when you can already read Python and SQL and reason about a dataset’s meaning.

What you will learn

• Transform data with PySpark and Spark SQL
• Explore Delta tables and catalog organization
• Build and schedule a data pipeline

Who this suits

Python and SQL knowledge are required, along with access to the relevant Azure environment. Suitable for a learner moving from local data work toward managed pipelines rather than a first programming introduction.

Before you enroll

Cloud and platform charges are separate from tuition. Versions, service names, and account features change. Use sample data, monitor resource usage, and verify permissions before uploading any organization’s information to a training workspace.

Enrollment is paid through Udemy. A completion certificate follows Udemy's course terms; it does not confer a professional qualification. Check current pricing and access before enrolling.

Put it into practice

Build a small pipeline from permitted sample files and test a duplicate or malformed record. Document the input assumptions, output checks, and resource-cleanup steps so another learner can understand the run.

Source and review

This overview is based on the provider's published information, checked on 11 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Udemy.

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