Databricks Certified - Data Engineer Associate (opens in a new tab)

• Virtual Learning Festival: 10 October - 31 October 2025 (opens in a new tab)

LEARNING PATHWAY 1: ASSOCIATE DATA ENGINEERING

1. Data Ingestion with Lakeflow Connect (opens in a new tab)

From Cloud Storage

• CREATE TABLE AS (CTAS)
• COPY INTO
• Auto Loader

From Databases

Lakeflow Connect Managed Connectors: Database Ingestion

From Enterprise applications

Lakeflow Connect Managed Connectors: SaaS Ingestion

2. Deploy Workloads with Lakeflow Jobs (opens in a new tab)

3. Build Data Pipelines with Lakeflow Declarative Pipelines (opens in a new tab)

• Lab

  • Processing Millions of Events from Thousands of Aircraft with One Declarative Pipeline (opens in a new tab)

    GitHub - Code (opens in a new tab)

• Tutorials

  • Databricks - Lakeflow Declarative Pipelines tutorials (opens in a new tab)

• References

  • Develop and debug ETL pipelines with the Lakeflow Pipelines Editor (opens in a new tab)
  • Azure Databricks documentation - Lakeflow Declarative Pipelines developer reference (opens in a new tab)
  • Lakeflow Declarative Pipelines SQL language reference (opens in a new tab)

4. Data Management and Governance with Unity Catalog (opens in a new tab)

Exam Guide

• DAB: Databricks Asset Bundles (opens in a new tab)

• Databricks Intelligence Platform

  

  

Section 1: Databricks Intelligence Platform

• 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.

Section 2: Development and Ingestion

• 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.

Section 3: Data Processing & Transformations

• Describe the three layers of the Medallion Architecture and explain the purpose of each layer in a data processing pipeline.
• Classify the type of cluster and configuration for optimal performance based on the scenario in which the cluster is used.
• Emphasize the advantages of LDP (for ETL process in Databricks).
• Implement data pipelines using LDP.
• Identify DDL/DML features.
• Compute complex aggregations and Metrics with PySpark Dataframes.

Section 4: Productionizing Data Pipelines

• Identify the difference between DAB and traditional deployment methods.
• Identify the structure of DAB.
• Deploy a workflow, repair, and rerun a task in case of failure.
• Use serverless for a hands-off, auto-optimized compute managed by Databricks.
• Analyzing the Spark UI to optimize the query.

Section 5: Data Governance & Quality

• Explain the difference between managed and external tables.
• Identify the grant of permissions to users and groups within UC.
• Identify key roles in UC.
• Identify how audit logs are stored.
• Use lineage features in UC.
• Use the Delta Sharing feature available with UC to share data.
• Identify the advantages and limitations of Delta sharing.
• Identify types of delta sharing - Databricks vs external system.
• Analyze the cost considerations of data sharing across clouds.
• Identify Use cases of Lakehouse Federation when connected to external sources.