Databricks Certified Data Engineer Associate
An exam-aligned certification program that trains you to complete introductory data engineering tasks on the Databricks Data Intelligence Platform — ETL with Spark SQL and PySpark, Auto Loader ingestion, Lakeflow Declarative Pipelines, workflow orchestration, and governance with Unity Catalog. Fully hands-on, mapped to the official exam guide.
The Databricks Certified Data Engineer Associate program is an instructor-led certification course that prepares you for the entry-level credential in the Databricks data engineering track. Every module is mapped directly to the official exam guide (live as of July 25, 2025), so what you practice is exactly what the exam assesses.
The exam measures your ability to use the Databricks Data Intelligence Platform to complete introductory data engineering tasks: understanding the platform, workspace, architecture, and capabilities; performing ETL with Apache Spark SQL or PySpark (extraction, complex data handling, user-defined functions); and deploying and orchestrating workloads with Databricks workflows. This course builds those skills hands-on — Auto Loader ingestion, the Medallion Architecture, Lakeflow Declarative Pipelines, Databricks Asset Bundles, serverless compute, and governance with Unity Catalog and Delta Sharing — with code samples in SQL where possible and PySpark (Python) otherwise, matching the exam's convention.
On completion you will be ready to sit the Databricks Certified Data Engineer Associate exam and to take on junior data engineering roles on the lakehouse.
Exam at a Glance
- Certification: Databricks Certified Data Engineer Associate | Offered by: Databricks
- Level: Associate — entry-level credential in the Databricks data engineering track
- Prerequisite: None required; course attendance plus ~6 months of hands-on Databricks experience is highly recommended
- Format: 45 scored multiple-choice questions (exams may include additional unscored items that do not affect your score) | Time limit: 90 minutes
- Delivery: Online proctored or test center | Test aids: none allowed
- Validity: 2 years (recertification by taking the current live exam)
- Code samples: SQL where possible, and PySpark (Python) otherwise
Exam Sections
- Section 1 — Databricks Intelligence Platform
- Section 2 — Development and Ingestion
- Section 3 — Data Processing & Transformations
- Section 4 — Productionizing Data Pipelines
- Section 5 — Data Governance & Quality
Course Highlights
- 100% aligned to the official exam guide (live as of July 25, 2025)
- Hands-on ETL with Spark SQL and PySpark on the Data Intelligence Platform
- Modern Databricks tooling: Auto Loader, Lakeflow Declarative Pipelines, Asset Bundles, serverless compute
- Medallion Architecture built end-to-end in labs
- Governance with Unity Catalog, Delta Sharing, and Lakehouse Federation
- Full-length mock exams with explanations
Learning Outcomes
- Navigate the Databricks platform and choose the right compute per use case
- Ingest data with Auto Loader and develop with Notebooks and Databricks Connect
- Build Medallion-style pipelines with Lakeflow Declarative Pipelines
- Compute complex aggregations with PySpark DataFrames and Spark SQL
- Deploy and operate workflows with Asset Bundles, serverless, and the Spark UI
- Govern and share data with Unity Catalog and Delta Sharing — and pass the exam with confidence
Job Opportunities
- Data Engineer
- Databricks Engineer
- ETL Developer
- Analytics Engineer
- Big Data Engineer
- Lakehouse Platform Engineer
Material Includes
- Class recordings
- Lab notebooks
- Mock exams
- Certificate
Who Can Join
- Aspiring Data Engineers
- SQL/BI Developers & Analysts
- Students & IT Professionals
Estimated Time: 4–5 Hours
Learning Objectives:- Understand the platform, workspace, architecture, and capabilities
- Choose the right compute for a given workload
- Enable features that simplify data layout decisions and optimize query performance
- Explain the value of the Data Intelligence Platform
- Identify the applicable compute to use for a specific use case
- Workspace tour: clusters, SQL warehouses, and platform features
- Compare compute options against workload scenarios
Estimated Time: 7–8 Hours
Learning Objectives:- Develop against Databricks from notebooks and local tooling
- Ingest data incrementally with Auto Loader
- Use Databricks Connect in a data engineering workflow
- Determine the capabilities of Notebooks functionality
- Classify valid Auto Loader sources and use cases
- Demonstrate knowledge of Auto Loader syntax
- Use Databricks' built-in debugging tools to troubleshoot a given issue
- Build an Auto Loader ingestion stream from cloud storage
- Debug a failing notebook job with the built-in tools
Estimated Time: 10–11 Hours
Learning Objectives:- Build Medallion-style ETL pipelines with Lakeflow Declarative Pipelines
- Transform and aggregate data with Spark SQL and PySpark
- Describe the three layers of the Medallion Architecture and the purpose of each layer in a data processing pipeline
- Classify the cluster type and configuration for optimal performance based on the scenario
- Explain the advantages of Lakeflow Declarative Pipelines (LDP) for ETL in Databricks
- Implement data pipelines using LDP
- Identify DDL (Data Definition Language) / DML features
- Compute complex aggregations and metrics with PySpark DataFrames
- Build a bronze→silver→gold pipeline with LDP
- Compute complex aggregations with PySpark DataFrames and UDFs
Estimated Time: 8–9 Hours
Learning Objectives:- Deploy and operate workflows the way production teams do
- Optimize queries using the Spark UI
- Identify the difference between Databricks Asset Bundles (DABs) and traditional deployment methods
- Identify the structure of Asset Bundles
- Deploy a workflow, and repair and rerun a task in case of failure
- Use serverless for hands-off, auto-optimized compute managed by Databricks
- Analyze the Spark UI to optimize a query
- Package and deploy a workflow with Databricks Asset Bundles
- Repair/rerun a failed task and tune a query via the Spark UI
Estimated Time: 7–8 Hours
Learning Objectives:- Govern data with Unity Catalog: permissions, lineage, and auditing
- Share data securely with Delta Sharing and federate external sources
- Explain the difference between managed and external tables
- Identify how to grant permissions to users and groups within Unity Catalog (UC)
- Identify key roles in Unity Catalog
- Identify how audit logs are stored
- Use lineage features in Unity Catalog
- Use the Delta Sharing feature available with Unity Catalog to share data
- Identify the advantages and limitations of Delta Sharing
- Identify types of Delta Sharing — Databricks-to-Databricks vs. external system
- Analyze the cost considerations of data sharing across clouds
- Identify use cases of Lakehouse Federation when connected to external sources
- Configure Unity Catalog permissions, roles, and lineage for a pipeline
- Share a dataset via Delta Sharing and query a federated external source
Estimated Time: 3–4 Hours
Learning Objectives:- Master the 45-question, 90-minute format and pacing
- Read exam code samples fluently — SQL where possible, PySpark otherwise
- Section-by-section review and weak-area drills
- Question strategy and elimination techniques (no test aids allowed)
- Recertification path: 2-year validity via the current live exam
- Full-length timed mock exams with detailed review
- Capstone: end-to-end lakehouse pipeline from ingestion to governed sharing
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