Databricks: Lakehouse, SQL & Python

In this one-day course, you'll get hands-on with the Databricks platform and the Lakehouse concept. You'll learn how Databricks accelerates collaboration between data teams and how to load, transform, and analyse data using notebooks, Delta Lake, and Databricks Workflows. You'll also discover how Databricks integrates seamlessly with cloud environments and how to use the platform for scalable data pipelines and analytics with SQL and Python as your primary working languages.

What you'll learn

• The core principles of the Databricks Lakehouse platform.
• Working with Databricks notebooks using SQL, Python, and Spark.
• Loading, transforming, and analysing data.
• Using Delta Lake for versioning, reliability, and data quality.
• Setting up access and permissions within Databricks.
• Automating processes with Databricks Workflows.

After this course you'll be able to:

• Navigate and work within the Databricks environment.
• Process data with Spark in notebooks.
• Build reliable datasets with Delta Lake.
• Build and run a simple data pipeline.
• Collaborate with other data professionals on the platform.

Who is this for

• Data engineers and data scientists.
• Data analysts and BI professionals.
• Teams looking to use Databricks to simplify and accelerate their data workflows.

Prerequisites

• Basic knowledge of SQL.
• Basic understanding of data warehousing.
• Experience with Python is a plus, but not required.

Programme

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

• Working with SQL and Python in Databricks.

Part 4 – Working with Delta Lake

• ACID transactions, reliability, and data quality.

Part 5 – Pipelines & Workflows

• Scheduling, automating, and executing tasks.

Part 6 – Best Practices & Q&A

• Integration, management, and next steps.