Discover what makes the lakehouse different from traditional architectures, how the medallion pattern organizes data, and where things live inside the Databricks platform.

Why the Lakehouse?

Discover lakehouse reliability features

Lakehouse vs. warehouse trade-offs

The Medallion Architecture

Explore the medallion layers

Match workloads to medallion layers

Platform Architecture

Control plane vs. data plane

Where are audit logs stored?

The Lakehouse Paradigm

Spin up the right cluster for the job, configure it for cost and performance, master the notebook environment, and connect your work to Git - all inside the Databricks workspace.

Cluster Types and Runtimes

Create and configure a cluster

Options

All-Purpose Cluster

Jobs Cluster

All-purpose or jobs cluster?

Managing and Configuring Clusters

Manage and configure a cluster

Restart

Terminate

Edit Configuration

Cluster management scenarios

Notebook Fundamentals

Build a multi-language notebook

Picking the right magic command

Collaboration and Version Control

Explore notebook version history

Notebook Versioning

Databricks Repos

Both

Repos vs. notebook versioning

Compute and Notebooks

Lock down your data with Unity Catalog, share it securely with Delta Sharing, and federate queries to external sources - all without copying a single byte.

Unity Catalog and Lineage

Unity Catalog hierarchy

Explore table metadata in Unity Catalog

Delta Sharing Fundamentals

Create a Delta Share

Add a table to a Delta Share

Delta Sharing: Types and Trade-offs

Native vs. open protocol sharing

Cross-cloud sharing costs

Lakehouse Federation

When to use Lakehouse Federation

Setting up a federated connection

Governance and Sharing

Package your work with Databricks Asset Bundles, deploy to production, and bring everything together in a capstone scenario.

From Manual to Automated Deployment

Examine a DAB project structure

DAB vs. traditional deployment

Inside an Asset Bundle

Inspect and modify an Asset Bundle

Trace a job from config to code

Putting It All Together

End-to-end lakehouse workflow

Balancing security, cost, and governance

Congratulations!

Deployment and Next Steps

Ready to work with a modern data platform that combines the best of data lakes and warehouses? In this course, you'll explore the Databricks Lakehouse from the ground up. Learn how the medallion architecture organizes raw data into trusted insights, spin up and configure clusters, and build multi-language notebooks connected to Git. Then dive into governance with Unity Catalog, share data securely using Delta Sharing, and deploy your work to production with Asset Bundles. By the end, you'll confidently navigate the Databricks platform and understand how its key components fit together.

Data lakes offer flexibility but lack reliability. Data warehouses deliver performance but can't handle unstructured data. The lakehouse combines both — and Databricks is where it all comes together. In this course, you'll explore the Databricks Lakehouse from the ground up, gaining hands-on experience with the platform's core components.
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<h2>Understand the Lakehouse Architecture</h2>
Start by discovering what sets the lakehouse apart from traditional approaches. You'll explore the medallion architecture — bronze, silver, and gold layers — that transforms raw, messy data into clean, business-ready insights. Then get oriented inside the Databricks workspace to understand how catalogs, schemas, and volumes organize everything.
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<h2>Master Compute and Notebooks</h2>
Learn to choose the right cluster for the job, configure autoscaling and auto-termination to control costs, and build notebooks that mix Python, SQL, and Markdown. You'll also connect your work to Git through Databricks Repos for version control and team collaboration.
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<h2>Govern and Share Data Securely</h2>
Explore Unity Catalog to manage access controls and track data lineage across your organization. Then use Delta Sharing to distribute data to partners — on Databricks or any other platform — and query external sources with Lakehouse Federation, all without copying a single byte.
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<h2>Deploy to Production with Asset Bundles</h2>
Wrap up by packaging your notebooks, pipelines, and jobs into Databricks Asset Bundles for repeatable, automated deployments. A capstone scenario brings everything together so you leave ready to apply these skills on the job.

Introduction to Databricks

Learn the Databricks Lakehouse hands-on: medallion architecture, clusters, Unity Catalog governance, Delta Sharing, and Asset Bundle deployment.

Introduction to Databricks Lakehouse

Explore the Databricks Lakehouse - from medallion architecture and clusters to governance, sharing, and deployment.

Associate Data Engineer in Databricks

A newly installed library isn't available in Python

Notebooks show stale cached data after a long session

The team is done for the day and no jobs are scheduled

A test cluster is no longer needed after a demo

The cluster was created with the wrong runtime version

The team needs a larger node type for a heavy workload

Cluster management scenarios

Introduction to Databricks Lakehouse

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