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Building Enterprise Applications with Databricks Apps

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.

Skill Level
Associate
Duration
3h
Prerequisites

In this course, the content was developed for participants with these skills/knowledge/abilities:  

• Basic familiarity with Databricks workspace operations, including navigation, creating notebooks, running code, and accessing workspace features through the UI

• Intermediate experience with Python programming, including virtual environments, package management, and web application development concepts

• Understanding of integrated development environments (IDEs) such as VSCode or PyCharm, including project setup, debugging, and code execution

• Basic knowledge of Unity Catalog concepts, including catalogs, schemas, tables, and data governance principles for accessing data objects programmatically

• Familiarity with command line interfaces, particularly Databricks CLI installation, configuration, and authentication using personal access tokens

• Basic understanding of web application frameworks and concepts, including user interfaces, form handling, and client-server interactions

• Beginner experience with service principals, identity management, and authentication concepts within cloud computing environments

• Basic knowledge of application deployment concepts, including local development, testing, and production deployment workflows

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Registration options

Databricks has a delivery method for wherever you are on your learning journey

Runtime

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Instructors

Instructor-Led

Public and private courses taught by expert instructors across half-day to two-day courses

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Learning

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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Scale

Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Get Started with Databricks for Machine Learning - Mandarin Chinese

在本课程中，您将培养使用 Databricks Data Intelligence Platform 执行机器学习工作流并支持数据科学工作负载所需的基础技能。您将从机器学习从业者的视角探索该平台，内容涵盖使用 Mosaic AI Feature Engineering 构建和管理特征、使用 MLflow 进行端到端模型生命周期管理，以及使用 Lakeflow Jobs 进行管道编排等主题。此外，您还将学习如何使用 Databricks AI Model Serving 进行实时模型推理，并通过 Genie Code - Data Science Agent Mode 体验 Databricks 透明、对话式的模型开发方式——在其中，您可以使用自然语言提示，直接在笔记本中生成、运行并迭代优化可执行的 ML 工作流。本课程包含由讲师主导的演示，并以一个全面的实验作为收尾，巩固贯穿全程所讲解的概念。

Languages Available: English | 日本語 | Português BR | 한국어

Paid & Subscription
3h
Lab
Onboarding
Data Engineer

Get Started with Databricks for Data Engineering

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.

Paid & Subscription
3h
Lab
Onboarding
Data Analyst

AI/BI for Data Analysts

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.

Languages Available: English | 日本語 | Português BR | 한국어 | Español| française

Paid & Subscription
3h
Lab
Associate

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.
FAQ