Data Engineering · Data Engineering

Databricks

This track covers Databricks' unified analytics platform: Lakehouse architecture, cluster management, notebooks, and Delta Lake. You'll learn to build and run ETL pipelines, optimize Spark jobs, and enable data quality. It's for data engineers and analysts who want to operationalize data workloads. You'll leave able to design multi-stage ELT workflows and govern data on the Databricks platform.

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Lesson outline

  1. 25
    Unity Catalog Schemas Basics

    Apply schemas with Unity Catalog basics in Databricks: learn core concepts, hands-on steps, and troubleshooting to prepare for the next lesson.

  2. 26
    Databricks SQL for Ad-Hoc Queries

    Learn to run fast, interactive SQL queries directly on your data lake with Databricks SQL. This lesson covers the essentials and hands-on steps to query data without writing Spark code, plus troubleshooting tips and what to learn next.

  3. 27
    Create a Live Table with Delta Live Tables

    Create a live table with Delta Live Tables — Databricks. Learn how to define and manage live tables, run a pipeline, and follow best practices in this hands-on tutorial.

  4. 28
    Declare Expectations for Data Quality

    Declare expectations for data quality checks — Databricks. This lesson shows you how to define and enforce data quality rules, run checks hands-on, troubleshoot issues, and prepare for next steps.

  5. 29
    Run Delta Live Tables Pipeline

    Run a Delta Live Tables pipeline in Databricks — step-by-step tutorial covering setup, deployment, and monitoring.

  6. 30
    Read Streaming Data with Structured Streaming

    Learn how to read streaming data with Structured Streaming in Databricks. This lesson covers the core concepts, step-by-step guidance, and a hands-on exercise to help you master the basics and prepare for the next lesson in the track.

  7. 31
    Streaming Aggregations with Watermarking

    Write streaming aggregations with watermarking in Databricks.

  8. 32
    Join Streaming and Batch Data

    Learn how to unify streaming and batch data in Databricks. This lesson shows you how to join a streaming DataFrame with a static batch table, handle late data, and choose the right join strategy for your pipeline.

  9. 33
    Apply Change Data Capture with MERGE

    Learn how to apply change data capture with MERGE in Databricks. This lesson covers the core concept, step-by-step implementation, and hands-on exercises to master incremental data processing.

  10. 34
    Medallion Architecture in Practice

    Build a medallion architecture in practice — Databricks. Learn to structure Bronze, Silver, and Gold layers for reliable data pipelines.

  11. 35
    Orchestrate Multi-Task Jobs

    Learn to orchestrate multi-task jobs with dependencies in Databricks. This lesson covers the core concepts, step-by-step implementation, hands-on exercise, and troubleshooting tips to build reliable data pipelines.

  12. 36
    Trigger Jobs on File Arrival

    In this Databricks lesson, learn how to trigger jobs automatically when files arrive in cloud storage using Auto Loader. Step-by-step instructions, troubleshooting tips, and what to study next.

    Databricks — step-by-step tutorials

    What you will find here

    This track walks through databricks in order — each lesson is server-rendered HTML you can read without JavaScript. Follow the outline, then practice in the browser IDE when a lesson links to runnable code.

    Tutorials vs quizzes and code samples

    Tutorials teach in sequence. For quick checks use quizzes. For copy-paste snippets see code samples. For deeper reading browse articles.