Data Engineering with Databricks

This is an introductory course that serves as an appropriate entry point to learn Data Engineering with Databricks. 

Below, we describe each of the four, four-hour modules included in this course.

Note: For the following four modules, the Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.

1. Data Ingestion with Lakeflow Connect

This course provides a comprehensive introduction to Lakeflow Connect, a scalable and simplified solution for ingesting data into Databricks from a wide range of sources. You’ll begin by exploring the different types of Lakeflow Connect connectors (Standard and Managed) and learn various data ingestion techniques, including batch, incremental batch, and streaming ingestion. You'll also review the key benefits of using Delta table and the Medallion architecture

Next, you’ll develop practical skills for ingesting data from cloud object storage using Lakeflow Connect Standard Connectors. This includes working with methods such as CREATE TABLE AS SELECT (CTAS), COPY INTO, and Auto Loader, with an emphasis on the benefits and considerations of each approach. You’ll also learn how to append metadata columns to your bronze-level tables during ingestion into the Databricks Data Intelligence Platform. The course then covers how to handle records that don’t match your table schema using the rescued data column, along with strategies for managing and analyzing this data. You’ll also explore techniques for ingesting and flattening semi-structured JSON data.

Following this, you’ll explore how to perform enterprise-grade data ingestion using Lakeflow Connect Managed Connectors to bring in data from databases and Software-as-a-Service (SaaS) applications. The course also introduces Partner Connect as an option for integrating partner tools into your ingestion workloads.

Finally, the course wraps up with alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with a strong foundation to support modern data engineering use cases.

2. Deploy Workloads with Lakeflow Jobs

Deploy Workloads with Lakeflow Jobs course teaches how to orchestrate and automate data, analytics, and AI workflows using Lakeflow Jobs as a unified orchestration platform within the Databricks ecosystem. 

• You will learn to design and implement data workloads using Directed Acyclic Graphs (DAGs), configure various scheduling options, and implement advanced workflow features such as conditional task execution, run-if dependencies, and for each loops. 

• The course covers best practices for creating robust, production-ready pipelines with proper compute selection, modular orchestration, error handling techniques, and fault-tolerant design-all natively integrated within the Databricks Data Intelligence Platform.

3. Build Data Pipelines with Apache Spark Declarative Pipelines

This course introduces users to the essential concepts and skills needed to build data pipelines using Apache Spark Declarative Pipelines (SDP) in Databricks for incremental batch or streaming ingestion and processing through multiple streaming tables and materialized views. Designed for data engineers new to Spark Declarative Pipelines, the course provides a comprehensive overview of core components such as incremental data processing, streaming tables, materialized views, and temporary views, highlighting their specific purposes and differences.

Topics covered include:

• Developing and debugging ETL pipelines with the multi-file editor in Spark Declarative Pipelines using SQL (with Python code examples provided)

• How Spark Declarative Pipelines track data dependencies in a pipeline through the pipeline graph

• Configuring pipeline compute resources, data assets, trigger modes, and other advanced options

Next, the course introduces data quality expectations in Spark Declarative Pipelines, guiding users through the process of integrating expectations into pipelines to validate and enforce data integrity. Learners will then explore how to put a pipeline into production, including scheduling options, and enabling pipeline event logging to monitor pipeline performance and health.

Finally, the course covers how to implement Change Data Capture (CDC) using the AUTO CDC INTO syntax within Spark Declarative Pipelines to manage slowly changing dimensions (SCD Type 1 and Type 2), preparing users to integrate CDC into their own pipelines.

4. DevOps Essentials for Data Engineering

This course explores software engineering best practices and DevOps principles, specifically designed for data engineers working with Databricks. Participants will build a strong foundation in key topics such as code quality, version control, documentation, and testing. The course emphasizes DevOps, covering core components, benefits, and the role of continuous integration and delivery (CI/CD) in optimizing data engineering workflows.

You will learn how to apply modularity principles in PySpark to create reusable components and structure code efficiently. Hands-on experience includes designing and implementing unit tests for PySpark functions using the pytest framework, followed by integration testing for Databricks data pipelines with Spark Declarative Pipeline and Jobs to ensure reliability.

The course also covers essential Git operations within Databricks, including using Databricks Git Folders to integrate continuous integration practices. Finally, you will take a high level look at various deployment methods for Databricks assets, such as REST API, CLI, SDK, and Declarative Automation Bundles (DABs), providing you with the knowledge of techniques to deploy and manage your pipelines.

By the end of the course, you will be proficient in software engineering and DevOps best practices, enabling you to build scalable, maintainable, and efficient data engineering solutions.