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Databricks Fundamentals 2026: Hands-On Projects

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Build lakehouse projects with SQL, PySpark, Delta Lake, Unity Catalog, Lakeflow, Genie Code, AI/BI and AWS or Azure.
1
1/5
(45) Ratings
77 students
Created by School of AI
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What you'll learn

  • Navigate Databricks Free Edition, create notebooks, and explore the Databricks Data Intelligence Platform.
  • Understand Data Lakehouse Architecture and build Bronze, Silver, and Gold data layers.
  • Write Databricks SQL queries to transform, analyze, and manage real-world business datasets.
  • Process and transform large datasets using Apache Spark, PySpark, and Spark DataFrames.
  • Build reliable Delta Lake tables using ACID transactions, MERGE operations, and Time Travel.
  • Implement data governance, access controls, and metadata management using Unity Catalog.
  • Design, automate, and monitor ETL pipelines using Databricks Lakeflow and Lakeflow Jobs.
  • Create no-code data transformation workflows using Lakeflow Designer.
  • Use Genie Code for AI-assisted SQL development, PySpark programming, and debugging.
  • Build interactive Databricks AI/BI dashboards and explore natural-language analytics with Genie Agents.
  • Explore machine learning, MLflow, foundation models, and AI agents within Databricks.
  • Build and deploy an end-to-end Databricks Lakehouse portfolio project using AWS or Azure.
This course includes:
9 total hours on-demand video
1 articles
1 downloadable resources
121 lessons
Full lifetime access
Access on mobile and TV
Certificate of completion
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Course content

Requirements

  • No prior Databricks experience is required. This course starts from the fundamentals.
  • No previous data engineering or big data experience is necessary.
  • Basic knowledge of Python or SQL is helpful but not mandatory.
  • A computer with a reliable internet connection and modern web browser is required.
  • A free Databricks account is recommended for completing the hands-on exercises.
  • An AWS or Azure account is optional for cloud deployment exercises; additional charges may apply.
  • Basic familiarity with databases, spreadsheets, or data analysis is beneficial but not required.
  • A willingness to experiment, troubleshoot problems, and complete hands-on projects is all you need to get started.

Description

This course contains the use of artificial intelligence.

Learn Databricks, SQL, PySpark, Delta Lake, Lakehouse Architecture, AI/BI, and Cloud Data Engineering Through Real-World Projects

Want to learn Databricks and build the practical skills needed for modern Data Engineering, Big Data Analytics, and Cloud Data Platforms? This hands-on course takes you from complete beginner to building real-world Databricks Lakehouse solutions using SQL, Python, PySpark, Apache Spark, Delta Lake, and Unity Catalog.

Whether you’re an aspiring Data Engineer, Data Analyst, Python Developer, or Cloud Engineer, you’ll learn how organizations process, transform, govern, automate, and analyze massive datasets using the Databricks Data Intelligence Platform.

And the best part? You’ll start with Databricks Free Edition, making it easy to begin learning without setting up expensive cloud infrastructure.

What You’ll Learn

This course goes beyond theory. You’ll build practical projects while learning essential Databricks Data Engineering technologies.

  • Databricks Fundamentals: Navigate the workspace, create notebooks, manage data, and understand the Databricks ecosystem.

  • Lakehouse Architecture: Explore Data Lakes, Data Warehouses, and Medallion Architecture with Bronze, Silver, and Gold layers.

  • Databricks SQL: Write queries, perform joins, analyze business data, and create reusable SQL transformations.

  • Apache Spark and PySpark: Process large datasets using Spark DataFrames, transformations, aggregations, and distributed computing.

  • Delta Lake: Build reliable Delta tables using ACID Transactions, MERGE, Time Travel, and incremental processing.

  • Unity Catalog: Understand Data Governance, access controls, metadata management, and data lineage.

  • Lakeflow: Build automated ETL Pipelines, schedule workflows, and monitor data processing.

  • Lakeflow Designer: Create visual, No-Code Data Pipelines using drag-and-drop transformations.

  • Genie Code: Explore AI-Assisted Data Engineering, SQL generation, code debugging, and productivity improvements.

  • Databricks AI/BI: Build interactive dashboards and explore Genie Agents for natural-language analytics.

  • Machine Learning and AI: Discover MLflow, foundation models, and introductory AI Agents on Databricks.

  • AWS and Azure Databricks: Learn introductory cloud deployment concepts and practices.

Learn by Building Real-World Projects

You’ll work with a fictional retail company facing realistic challenges involving sales, customers, inventory, and business reporting.

Throughout the course, you’ll build SQL Analytics Projects, develop PySpark ETL Pipelines, implement Delta Lake Medallion Architecture, automate workflows with Lakeflow, and create Business Intelligence Dashboards.

Your final End-to-End Databricks Capstone Project brings everything together into a portfolio-ready Retail Intelligence Platform, complete with data ingestion, transformations, governance, analytics, and deployment documentation.

Who Should Take This Course?

This course is designed for Databricks Beginners, aspiring Data Engineers, SQL Developers, Data Analysts, Software Engineers, and professionals exploring Cloud Data Engineering, Data Lakehouse Architecture, and AI-Powered Analytics.

No previous Databricks experience is required. Basic familiarity with Python or SQL is helpful, but beginners are welcome.

Start Building Your Databricks Skills Today

By completing this course, you’ll gain practical experience with modern Data Engineering Workflows, understand how enterprise Lakehouse platforms operate, and develop projects you can confidently discuss during technical interviews.

Stop watching disconnected tutorials. Start building real solutions with Databricks Fundamentals 2026!

Who this course is for:

  • Beginners who want to learn Databricks from scratch through practical, hands-on projects.
  • Aspiring Data Engineers looking to develop job-relevant skills in modern data engineering.
  • Data Analysts interested in Databricks SQL, Lakehouse Architecture, and business intelligence.
  • Python Developers who want to learn PySpark, Apache Spark, and distributed data processing.
  • SQL Developers and Database Professionals transitioning into cloud data engineering.
  • Cloud Engineers interested in building data platforms using Azure Databricks or Databricks on AWS.
  • Software Engineers exploring ETL pipelines, Delta Lake, and scalable data architectures.
  • AI and Machine Learning enthusiasts interested in MLflow, Genie Code, and AI-powered analytics.
  • IT Professionals and career changers seeking practical experience with Databricks and modern data platforms.
  • Students and recent graduates who want to build portfolio-ready Databricks projects.
  • Business Intelligence Developers looking to create interactive dashboards using Databricks AI/BI.
  • Data professionals preparing for entry-level Databricks roles or building foundational knowledge for Databricks certification studies.
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