Factored
Databricks Batch & Streaming
About This Course
This course moves data engineers from foundational understanding to production-ready expertise in building data pipelines on Databricks. Whether you are starting a new role or expanding your technical depth, this course equips you to architect, implement, and optimize both batch and streaming data platforms.
The course combines Databricks Partner Academy learning with original deep-dive material. You will start with batch processing fundamentals, master Delta Lake and advanced ETL patterns, then progress to streaming architectures using Spark Structured Streaming. Throughout, you will learn to apply governance, optimize performance, and monitor production pipelines at scale.
What You Will Learn
In the Batch Processing section, you will understand bounded data processing paradigms, master data ingestion techniques, and apply Delta Lake for ACID-compliant transformations. You will architect scalable pipelines using Databricks Jobs and Delta Live Tables, implement quality governance through constraints and expectations, and apply advanced patterns like slowly changing dimensions and change data capture.
In the Streaming section, you will grasp continuous data processing concepts and build end-to-end streaming applications with Structured Streaming. You will ingest data from multiple sources, apply time-based windowing and watermarking, manage stateful operations and joins, and operationalize streaming pipelines through monitoring, optimization, and governance. By the end, you will be able to choose between batch and streaming architectures based on business requirements and implement production-grade pipelines in either paradigm.
Who This Course Is For
This course is designed for data engineers with SQL and Python fundamentals who are working or planning to work on Databricks. You should be comfortable with basic data transformation concepts and have some exposure to distributed computing. No prior Databricks experience is required. The course assumes you can navigate a cloud environment and are familiar with version control workflows. Advanced distributed systems knowledge or cluster administration is not assumed; the course teaches what you need to know.
How the Course Is Structured
The course is organized into two major chapters. Chapter 1 covers Batch Processing across seven subsections: foundational concepts, data ingestion, Delta Lake fundamentals, data quality and governance, pipeline orchestration, performance tuning, and advanced ETL patterns. Chapter 2 covers Streaming in six subsections: streaming fundamentals, data ingestion for streams, Spark Structured Streaming programming, streaming Delta Lake management, monitoring and optimization, and governance for continuous pipelines.
Each subsection combines conceptual learning with hands-on technical depth. Where Databricks Partner Academy courses cover a topic, that subsection opens with the course as a broad onramp, then continues with original material that extends into production concerns. Most subsections contain 3 to 5 focused units, allowing you to build knowledge progressively without information overload.
Time Commitment
This is a comprehensive course designed for thorough learning. Budget 40 to 60 hours of engaged study time to move through all material, work through code examples, and complete assessments. The Batch section typically requires 20 to 30 hours, while the Streaming section requires 20 to 30 hours. You can move at your own pace, pausing to experiment in a Databricks workspace or dive deeper into areas most relevant to your role.