Databricks Data Engineering

Course Overview

Databricks Data Engineering is a hands‑on, cloud‑native course that teaches learners how to build scalable, reliable, and high‑performance data pipelines using the Databricks Lakehouse Platform. The course covers Delta Lake, Apache Spark, ETL/ELT workflows, data ingestion, orchestration, optimization, and production‑grade data engineering practices. Learners gain practical experience with notebooks, SQL, Python, and the Databricks workspace to design end‑to‑end data solutions.

Target Audience

This course is ideal for:

• Aspiring data engineers and big‑data developers

• Data analysts and data scientists transitioning into engineering roles

• Cloud engineers working with Azure Databricks, AWS Databricks, or GCP Databricks

• ETL developers modernizing pipelines to Spark‑based architectures

• Anyone preparing for Databricks Data Engineer Associate or Professional certifications

Course Outcomes

By the end of this course, learners will be able to:

• Understand the Databricks Lakehouse architecture and its advantages

• Build scalable ETL/ELT pipelines using Apache Spark (SQL, PySpark)

• Use Delta Lake for ACID transactions, schema evolution, and time travel

• Ingest batch and streaming data using Auto Loader and Structured Streaming

• Optimize pipelines using Z‑Ordering, caching, and cluster configuration

• Implement data governance using Unity Catalog

• Orchestrate workflows using Databricks Jobs and Delta Live Tables

• Monitor, debug, and productionize data pipelines

• Apply best practices for performance, reliability, and cost efficiency

• Demonstrate skills aligned with Databricks Data Engineer certification exams