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Databricks Certified Data Engineer Associate
Databricks is a unified data intelligence platform built on the lakehouse architecture, combining data warehouse reliability with data lake flexibility to support data engineering, analytics, and AI workloads at scale.
This path prepares learners for the Databricks Certified Data Engineer Associate exam, covering data ingestion and loading, transformation and modeling with PySpark and SQL, orchestrating pipelines with Lakeflow Jobs, implementing CI/CD, and applying governance and security controls using Unity Catalog.
This learning path is actively in production. More content will be added to this page as it gets published and becomes available in the library. Planned content includes:
\- Databricks Certified Data Engineer Associate: Databricks Intelligence Platform (video course)
\- Databricks Certified Data Engineer Associate: Data Ingestion and Loading (video course)
\- Databricks Certified Data Engineer Associate: Data Transformation and Modeling (video course)
\- Databricks Certified Data Engineer Associate: Working with Lakeflow Jobs (video course)
\- Databricks Certified Data Engineer Associate: Implementing CI/CD (video course)
\- Databricks Certified Data Engineer Associate: Troubleshooting, Monitoring, and Optimization (video course)
\- Databricks Certified Data Engineer Associate: Governance and Security (video course)
Content in this path
Databricks Certified Data Engineer Associate
Watch the following courses to get learning about the Databricks Data Intelligence Platform and prepare for the Databricks Certified Data Engineer Associate certification!
Try this certification path for free
Build confidence to ace your certification exam with a variety of prep tools, including video courses, labs, and practice exams.
What You'll Learn
- \### What You Will Learn
- \- How to navigate the Databricks Data Intelligence Platform, including its architecture, Delta Lake, Unity Catalog, and compute services
- \- How to ingest and load data using batch, streaming, and incremental patterns with Auto Loader, Lakeflow Connect, and COPY INTO
- \- How to clean, transform, and model data using PySpark and SQL, including joins, deduplication, and building Silver and Gold layer tables
- \- How to orchestrate and schedule data pipelines using Lakeflow Jobs, including control flows and triggers
- \- How to implement CI/CD workflows using Databricks Git integration and Databricks Asset Bundles
- \- How to troubleshoot, monitor, and optimize pipeline performance using the Spark UI and Lakeflow Jobs run history
- \- How to configure governance and security controls in Unity Catalog, including access controls, row-level security, and column masking
- There are no formal prerequisites required for the Databricks Certified Data Engineer Associate exam, though Databricks recommends six months of hands-on platform experience plus related training. Candidates should be comfortable with:
- \- Working knowledge of SQL query syntax (SELECT, WHERE, GROUP BY, ORDER BY, JOIN)
- \- Basic familiarity with Python
- \- General understanding of ETL concepts and data pipeline design
- \- Familiarity with cloud computing concepts (storage, compute, identity management)
- Recommended Databricks-specific knowledge:
- \- The Databricks Lakehouse Platform, Delta Lake, and Unity Catalog
- \- Databricks compute options (clusters, SQL warehouses, serverless)
- \- Data ingestion methods including Auto Loader and Lakeflow Connect
- \- Lakeflow Jobs for pipeline orchestration
- \- Git integration and Databricks Asset Bundles for CI/CD
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