Get Started with Databricks for Data Engineering
This course introduces the foundational skills needed to perform a basic data engineering workflow on the Databricks Data Intelligence Platform. You will explore the workspace, work with Unity Catalog, and learn the day-to-day building blocks a data engineer uses on Databricks.
The course follows a hands-on path. You start by getting oriented in the workspace, then work through paired Demo and Lab notebooks for each topic. Demos walk you through a concept with the instructor or a guided notebook.
The content covers:
• The Databricks Data Intelligence Platform and how the Databricks workspace, Unity Catalog, and notebooks fit together.
• Creating and managing Delta Lake tables.
• Modifying data with INSERT, UPDATE, and DELETE.
• Exploring version history and time travel on UC tables.
• Ingesting data with Lakeflow Connect options: CTAS, the Upload UI, and COPY INTO.
• Building a Medallion Architecture pipeline that transforms data through Bronze, Silver, and Gold layers.
• Automating pipelines with Lakeflow Jobs.
• (Bonus) Building a declarative pipeline with Apache Spark™ Declarative Pipelines.
By the end of the course, you will be comfortable creating Delta Lake tables, ingesting data into them, transforming that data through a Medallion pipeline, and automating the whole thing as a scheduled job.
• Basic familiarity with cloud data platforms. You understand what a database, table, and query are at a conceptual level.
• Working knowledge of SQL. You can read and write SELECT statements with WHERE, GROUP BY, and aggregates, and you understand INSERT, UPDATE, and DELETE.
• General awareness of data engineering concepts like data ingestion, transformation, and pipelines is helpful but not required — the course covers the basics.
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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