Building and Sharing Secure Data and AI Applications with Databricks Apps

Overview

In the rapidly evolving landscape of data and AI, transforming insights into actionable, user-friendly applications is paramount. Traditionally, this “last mile” has been complex, requiring diverse infrastructure, security, and deployment skills. ‘Databricks Apps’, a groundbreaking new feature on the Databricks platform, revolutionizes how organizations build and share secure data and AI applications.

This blog post will explore Databricks Apps, its core capabilities, generation process, advantages, and common questions.

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Introduction

Databricks Apps is a significant advancement on the Databricks Data Intelligence Platform, designed to simplify and accelerate the development and deployment of secure data and AI applications. It enables developers to build interactive, user-facing tools directly within the Databricks environment, eliminating the need for separate infrastructure or complex security configurations.

It leverages the power of the Databricks Lakehouse, providing seamless access to governed data, robust AI/ML capabilities, and scalable compute. This allows apps to interact directly with Delta Lake tables, tap into Model Serving for AI models, and orchestrate Lakeflow Jobs, all within a unified, secure ecosystem.

Key innovations include its serverless architecture and deep integration with Unity Catalog, which is Databricks’ unified governance solution. This ensures applications inherit the same granular access controls, auditing, and lineage established for underlying data assets, making them exceptionally secure and compliant by design.

How to Generate Databricks Apps?

Generating and deploying a Databricks App involves a streamlined process that integrates with your existing development workflows, favoring local development and version control.

Here’s a conceptual breakdown of how to generate a Databricks App:

Prerequisites:

Azure Databricks Workspace: Enterprise or Premium tier with appropriate permissions.
Databricks CLI: Installed and configured locally to interact with your workspace.
Python (and Frameworks): Python 3.11+ locally, with frameworks like Streamlit, Dash, or Gradio.
Git (Recommended): For version control and collaboration.

Development Workflow:

Local Development Environment Setup: Create a local directory, install Python packages in a virtual environment, and initialize Git.
Choose Your Framework and Build Your App: Use Python libraries like Streamlit, Dash, or Gradio. Your ‘py’ will contain UI, data interaction, and AI model logic, interacting with Databricks SQL or Model Serving.
Define app.yaml (App Configuration): This crucial file, at your app’s root, configures runtime. Key fields include name, entrypoint (e.g., py), resources (Databricks resources and permissions), environment variables, and dependencies (referencing requirements.txt).
Manage Dependencies (requirements.txt): List all Python package dependencies here; Databricks Apps will install them upon deployment.
Local Testing and Debugging: Test locally using your framework’s run command (e.g., streamlit run app.py). ‘databricks apps run-local’ offers integrated testing.

Deployment to Databricks:

Create the App in Databricks (Initial Setup): Use the Databricks CLI (databricks apps create –json ‘{ “name”: “my-awesome-app” }’) or Databricks UI (+ New > App).
Sync Code to Databricks Workspace: Upload your local source code using:
- databricks sync . “/Workspace/Users/your_email@example.com/databricks_apps/my-awesome-app”.
Deploy the Application: Deploy using the Databricks CLI:
- databricks apps deploy my-awesome-app –source-code-path “/Workspace/Users/your_email@example.com/databricks_apps/my-awesome-app”.

Databricks will provision, compute, install, and launch dependencies.

Access and Share: Retrieve your app’s unique URL using:
- databricks apps get my-awesome-app | jq -r ‘.url’

and share it. Unity Catalog permissions control access.

Combining local development with seamless deployment to a serverless, governed platform, this structured approach makes creating and distributing data and AI applications on Databricks incredibly efficient.

Advantages of Databricks Apps

Databricks Apps offers numerous benefits for operationalizing data and AI:

Simplified Deployment and Management (Serverless): No need to provision, configure, or scale infrastructure. Databricks handles compute, reducing operational overhead.
Built-in Security and Governance with Unity Catalog: Apps inherit granular data permissions, ensuring users only access authorized data. All interactions are auditable, and policies are centrally enforced, guaranteeing robust security and compliance.
Faster Time-to-Value: By abstracting infrastructure and security, Databricks Apps significantly accelerates development and deployment, enabling rapid iteration and quicker delivery of insights.
“Apps Where Your Data Lives”: Apps run within your Databricks workspace, close to your data in the Lakehouse. This minimizes data movement, reduces latency, and enhances security by preventing data egress.

Databricks Apps simplifies the entire lifecycle of building and sharing data and AI applications, making it more secure, efficient, and accessible.

Conclusion

Databricks Apps represents a pivotal leap forward in how organizations leverage their data and AI assets. By offering a fully managed, serverless, and inherently secure platform for building and sharing interactive applications, it tackles the long-standing “last mile” challenge of democratizing data and AI insights.

Drop a query if you have any questions regarding Databricks Apps and we will get back to you quickly.

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About CloudThat

CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

FAQs

1. What kind of applications can I build with Databricks Apps?

ANS: – You can build a wide range of internal tools and applications, including:

Interactive data visualizations and BI dashboards.
Retrieval-Augmented Generation (RAG) chat applications powered by LLMs.
Custom configuration interfaces for Lakeflow jobs.
Data entry forms backed by Databricks SQL.
Business process automation tools combining various Databricks services.
Custom operational tools for alert triage and response.

2. Which programming languages and frameworks are supported?

ANS: – Databricks Apps primarily supports Python and popular Python frameworks like Streamlit, Dash, and Gradio. The platform also offers flexibility to use other frameworks by defining your application’s entry point and dependencies.

WRITTEN BY Yaswanth Tippa

Yaswanth Tippa is working as a Research Associate - Data and AIoT at CloudThat. He is a highly passionate and self-motivated individual with experience in data engineering and cloud computing with substantial expertise in building solutions for complex business problems involving large-scale data warehousing and reporting.