Databricks for Data Engineers

In this one-day course, you will be introduced to the Databricks platform and the Lakehouse concept, and learn how Databricks accelerates collaboration between data teams. After a brief overview of the architecture, you will get hands-on experience loading, transforming and analyzing data. You’ll work with notebooks, Delta Lake and Databricks Workflows to automate tasks, and see how Databricks integrates with existing cloud environments to support scalable data pipelines and analytics.

What you learn

• Core concepts of the Databricks Lakehouse.

• Working with Databricks notebooks (SQL, Python and Spark).

• Loading, transforming and analyzing data in Databricks.

• Using Delta Lake for versioning, reliability and data quality.

• Setting up authorization and access control in Databricks.

• Automating processes with Databricks Workflows.

After this course you will be able to:

• Navigate and work effectively within the Databricks environment.

• Process data with Spark in notebooks using SQL and Python.

• Apply Delta Lake to create reliable, versioned datasets.

• Build and run a simple data pipeline on the platform.

• Collaborate with other data professionals using shared Databricks assets.

For whom

• Data engineers and data scientists.

• Data analysts and BI professionals.

• Anyone who wants to use Databricks to simplify and accelerate data workflows.

Prerequisites

• Basic knowledge of SQL.

• Basic understanding of data warehousing concepts.

• Some experience with Python is a plus, but not required.

Content (global program)

Part 1

• Introduction to the Lakehouse.

• Concept, architecture and positioning of Databricks.

Part 2

• Exploring the platform.

• Clusters, notebooks and the workspace.

Part 3

• Hands-on: loading & transforming data.

• Using SQL and Python in Databricks.

Part 4

• Working with Delta Lake.

• ACID transactions, reliability and data quality.

Part 5

• Pipelines & Workflows.

• Scheduling and automating tasks with Databricks Workflows.

Part 6

• Best practices & Q&A.

• Integration, management and next steps.