Building Persistent Memory for AI Agents with Lakebase on Databricks

This course introduces Databricks Apps, a new feature that empowers developers to build and deploy secure data and AI applications directly on the Databricks platform. It is primarily designed for data scientists, data engineers, and developers who need to create custom applications that make data insights accessible to non-technical users within their organizations.

The course begins with an overview of the main components of Databricks Apps, followed by the deployment of a simple app that integrates with an external service. Next, you will learn how to build and deploy intelligent, data-driven applications that integrate with Databricks platform features. The course then covers how to develop apps outside the Databricks environment. It concludes with a detailed treatment of best practices for implementing secure and reliable enterprise apps.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

This course introduces the foundational skills needed to perform a basic data engineering workflow on the Databricks Data Intelligence Platform. You will explore the workspace, work with Unity Catalog, and learn the day-to-day building blocks a data engineer uses on Databricks.

The course follows a hands-on path. You start by getting oriented in the workspace, then work through paired Demo and Lab notebooks for each topic. Demos walk you through a concept with the instructor or a guided notebook.

The content covers:

• The Databricks Data Intelligence Platform and how the Databricks workspace, Unity Catalog, and notebooks fit together.

• Creating and managing Delta Lake tables.

• Modifying data with INSERT, UPDATE, and DELETE.

• Exploring version history and time travel on Delta tables.

• Ingesting data with Lakeflow Connect options: CTAS, the Upload UI, and COPY INTO.

• Building a Medallion Architecture pipeline that transforms data through Bronze, Silver, and Gold layers.

• Automating pipelines with Lakeflow Jobs.

• (Bonus) Building a declarative pipeline with Spark Declarative Pipelines.

This course teaches data analysts how to design, build, publish, and operate AI/BI Dashboards in Databricks. AI/BI Dashboards combine governed Unity Catalog data with interactive visualizations, filters, and Genie integration so business users can explore answers without writing code.

The course follows a single end-to-end build. You start with source tables in Unity Catalog and finish with a published, monitored multi-page dashboard. Along the way you learn how dashboards fit into the broader Databricks AI/BI product family and where Genie, datasets, visualizations, and filters each fit in the workflow.

The content covers:

• AI/BI Dashboard fundamentals and how they relate to Genie and the rest of the Databricks platform.

• Exploring source data in Unity Catalog and designing reusable dashboard datasets with SQL.

• Authoring visualizations (KPIs, trends, breakdowns) and laying out a clean multi-page dashboard.

• Using Genie Code to draft SQL, charts, and filters from natural language prompts.

• Adding filters to make dashboards interactive and responsive to viewer questions.

• Publishing, sharing, and managing permissions so the right people can view and edit the dashboard.

• Running the dashboard in production with scheduled refresh, caching, and usage monitoring.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

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