Apache Spark Programming with Databricks
This program aligns to the Apache Spark developer learning pathway on Databricks and prepares teams to build, test, and optimize Spark applications more effectively. It focuses on Spark fundamentals, DataFrame APIs, Spark SQL, transformation logic, and the development habits needed for production-oriented distributed data work.
Certification
Databricks Certified Associate Developer for Apache Spark
Delivery
Virtual, On-site, or Hybrid
Duration
3 days
Product
Databricks Data Intelligence Platform
Role
Apache Spark Developer
Databricks
Apache Spark DeveloperPySpark, Spark SQL, distributed apps
Databricks Spark
Best Fit
Audience Profile
Who This Program Is For
Built for practitioners who need to write, validate, and improve Spark applications on Databricks using practical distributed-processing patterns.
Overview
Program Summary
Databricks Spark developer program aligned to Apache Spark application-development workflows and associate-level Spark certification outcomes.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Learn Spark programming foundations
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Module 1
Learn Spark programming foundations
Build the core concepts and development model behind Apache Spark applications on Databricks before moving into larger workloads.
- Spark programming foundations
2Module 2
Use DataFrames and Spark SQL effectively
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Module 2
Use DataFrames and Spark SQL effectively
Work with the primary APIs developers use for transformation, querying, and iterative Spark data-processing logic.
- Working with DataFrames and Spark SQL
3Module 3
Build maintainable Spark transformations
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Module 3
Build maintainable Spark transformations
Apply reusable coding patterns for transformation-heavy Spark jobs and notebook-to-production development workflows.
- Building transformations and reusable logic
4Module 4
Test and tune Spark application behavior
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Module 4
Test and tune Spark application behavior
Improve confidence in Spark jobs through troubleshooting, validation, and performance-aware development choices.
- Testing and improving Spark applications
Coverage Areas
Topic Coverage
Coverage Item 1
Spark programming foundations
Coverage Item 2
Working with DataFrames and Spark SQL
Coverage Item 3
Building transformations and reusable logic
Coverage Item 4
Testing and improving Spark applications
Customization
Adapt This Program for Your Team
We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.
- •Use your preferred language emphasis where applicable
- •Add medallion-style data-processing scenarios for engineering teams
- •Extend into testing, debugging, and optimization standards for Spark projects
