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How to Vibe Code Databricks Apps with Lakebase in Minutes

Summary

  • Full-Stack Apps in Databricks: Build complete web applications with frontend, logic, and transactional database inside the secure Databricks environment using VibeCoding.
  • Lakebase Transactional Layer: Use managed Postgres (Lakebase) as the transactional database for stateful applications with deep integration to Databricks data and AI.
  • AI-Assisted Development: Leverage AI to code applications 4x faster with Context Engineering workflow, building startup-speed apps while maintaining enterprise security.

How to Vibe Code Databricks Apps with Lakebase in Minutes

Watch: How to Vibe Code Databricks Apps with Lakebase in Minutes
In this tutorial, you will learn how to build interactive data and AI applications directly within the Databricks environment using Lakebase (managed Postgres), Databricks Apps, and AI-assisted development in your IDE.
🎯 What You'll Learn:
• Why traditional AI app builders create expensive data duplication
• How to build full-stack apps inside your secure Databricks environment
• The 4-step Context Engineering workflow for productive AI coding
⏱️ CHAPTERS:
📚 Resources:
• Apps Cookbook: https://apps-cookbook.dev
• GitHub repo with Databricks Apps Templates: https://github.com/databricks/app-templates
• Blogpost with working code: https://www.databricks.com/blog/how-use-lakebase-transactional-data-layer-databricks-apps
• Hands-On Guide to Apps on Databricks: https://www.databricks.com/resources/ebook/hands-guide-apps-databricks

Chapters

FAQs

What is VibeCoding on Databricks?

VibeCoding is AI-assisted development that lets you write full-stack applications by describing what you want rather than writing every line of code. This video demonstrates building stateful applications at startup speed.

What is Lakebase and why use it for Databricks Apps?

Lakebase is a fully managed Postgres database that serves as the transactional layer for Databricks Apps. It enables stateful applications with deep integration to your data and AI environment, eliminating the need to duplicate data outside your secure lakehouse.

Can you show a real example of a Databricks App?

Yes. This video builds a knowledge testing application that processes uploaded documents, extracts key ideas, generates Q&A pairs stored in Lakebase, and allows users to test knowledge and track progress.

What are typical use cases for Databricks Apps?

Use cases include HR onboarding tools, employee training platforms, workshop knowledge assessments, and any stateful application that needs to connect directly to your data without moving it outside your secure environment.

Full transcript

0:00 The Enterprise AI App Challenge

Here is a mind-blowing stat. One in four Y Combinator startups are already shipping with 95% AI generated code. So if you think VIP coding is just for non-technical users building simple applications, think again. The startups building applications in days not months and enterprises are racing to catch up. But here is the challenge. Most AI app builders, whether it's Replet, Lovable, or others, sit outside of your core data environment.
And here where it gets expensive. Double storage costs from duplicated data, operational overhead from managing applications outside of your environment, and security vulnerabilities that bypass all your existing controls. So the real challenge isn't about building an app. It's about building one that connects directly to your data and runs under the same controls as the rest of your enterprise stack. And that's where datab bricks comes in.
Have you ever tried to wipe code an app on datab bricks? And I'm not talking about generating Python code for a notebook or creating a dashboard. I mean, a full stack application with a web front end, a logic layer, and a transactional database, all deeply integrated with your data and AI environment. In this video, I'm going to show you exactly how to build at startup speed while keeping enterprisegrade security using what I call databris vibe coding stack.
Lakebase as a fully managed Postgress transactional database. Datab bricks apps as a secure runtime and UI and your ID with AI assistance to bring the logic to life. My goal is to show you the end toend build process plus share tips for making AI collaboration truly productive. Let me show you exactly what we are building. We are creating a knowledge testing application. It's a perfect first project because it touches every part of the stack.
Database reads and writes then interaction and an LLM integration for content generation. So here's the flow. You upload the document can be technical documentation, policies or anything else. processes the file, extracts key ideas and then generates question and answer pairs. Those Q&As are stored in our transactional league based database. Then you can use this to test your knowledge and track progress. It's a simple idea, but it's a real stateful application.

1:54 Project Overview: Knowledge Testing App

And it's flexible. You can adopt it for HR on boarding, employee training, or a workshop knowledge checks. This is the kind of app that could replace expensive training platforms. It's a classic three- tier architecture, but everything lives inside your secure datab bricks environment. Let's break it down. First, the database layer. Lakebase. No more need for data duplication. Lakebase connects directly to your existing data.

2:54 Architecture: The Vibe-Coding Stack Explained

It's built for fast transactional workloads and loved by developers for its open-source Postgress compatibility. Lakebase is designed to power intelligent applications built on a lakehouse without the complexity of a traditional database management. It eliminates the need for customuilt complex ETL pipelines to integrate transactional data with analytics and a IML environment. And what makes it truly stand out? Its architecture that high throughput, low latency and reliability with enterprise security features.
Next, we have data bricks apps. Our secure serverless runtime for the web and application layer. Apps run on provision serverless compute so you don't have to manage it yourself. You can start quickly with templates permissions and audit trails are built in. Applications also come with production ready git version control and CI/CD pipelines making it simple to manage and scale. Finally the logic layer where development happens.
Here's the key. We are not just asking AI for code. We are practicing context engineering. Giving the model the right information in the right format so it can perform effectively. I will be working in cursor but the same principles apply in VS code with the data bricks extension or pretty much any ID of your choice with AI coding agents. So instead of vague prompts, we will supply targeted context, documentation snippets, clear task outlines, and even provide some simple queries structured in the right order.
Think of it less as prompting and more architecting a workflow between you and AI coding agents. That's what makes AI coding productive, consistent, and reliable. Now, let's build it together. Step one, create a lakebased instance. Every app needs a database. So let's provision ours. In the datab bricks workspace, click compute on the left navigation pane. Select the lakebase posgress tab and then create. This brings up the configuration page.

4:59 Setup: Lakebase + Databricks Apps (5 Minutes)

First give your database a name. Next is the instance size. You can see a range of options here. For this demo, the smallest size is more than enough and it helps to keep our costs down. This is not a permanent choice, so you can resize it later. We will leave the other settings as default for now. Hit create and databicks provisions a fully managed production grade posgress instance. This usually takes a couple minutes.
While the database spins up, let's launch the runtime for our app. Head over to compute, then apps tab, and click visualization. The flask template is ideal if you are building a back end with REST API. But today we want something light and flexible. I will choose streamlid with a basic hello world template. Give it a name and hit create. So now we've got our scaffold. Now let's wire things together. We need to explicitly grant our application access to resources and configure permissions.
In the app settings, open the app resource tab. First attach our wake base instance from the drop-down. Then let's add an LLM. We need to select an endpoint. You can see the whole list of available models here. proprietary models from OpenAI, Anthropic, Google, and also powerful open-source options like Llama. This flexibility is a huge advantage. I'm going to stick with Cloud for Sonnet. Our app runs under a service principle with explicitly granted permissions to the database and LLM endpoints.
All users inherent the service principle access rights. So you can control exactly what the app can access through Unity catalog governance. And with that, our infrastructure is done. In just a few clicks, we have a runtime, a UI scaffold, and a connected database with LM ready to go. Now comes the fun part, actually building the app. Before I write a single prompt, I collect the exact resources I will need. That might be documentation for connecting to lakebase, a specific Python library documentation for parsing PDFs or might be some kind of API calls for the LM and I do this research and identify resources.

7:27 The Context Engineering Workflow

Then I provide this information. This grounds the model in real up-to-date information and prevents it from inventing outdated or incorrect code patterns. So if you know you will reference snippet code more than once then create a file and just paste it directly rather than providing a link. One more important best practice here limit the scope of your context. I set boundaries in my prompts like use only the libraries provided.
Don't import extras. Too much irrelevant material only confuses the model. Give it exactly what's needed for the step you are working on. Next, I describe the goal and requirements and ask the model to generate a plan. For example, I am building an employee quiz app. Here is a list of features and documents. Think step by step and draft a to-do plan and save it in my rep. Once the model lays out a to-do, you can review it and edit it.
That plans becomes your shared blueprint and keeps the agents on track. So, the first step is to create the database schema. So I'm going to add this prompt based on our application requirements. Generate SQL statements to create question and answers tables. And here I'm going to reference documentation from datab bricks cookbook. It's a great resource if you are building databicks apps. Once you have the SQL, open the databicks SQL editor.
Click new query and select the correct database. Here you will need to replace the placeholder client ID with the application service principle. You will find it in the environment tab of your application. If for some reason this generated SQL fails, don't waste your time copy pasting from ID or other uh coding agents. Just ask databicks assistant to diagnose your error. it will suggest you a fix and then when you get working code then paste it back to the model so it remembers the correct version.
This feedback injection keeps the context clean and prevents the model from repeating the same mistakes. Something will always break and that's fine. The key is to help model to troubleshoot it. Don't just say fix it. Instead give it as much context as you can. For example, share logs, error messages, or failing test outputs with the model. The more specific you are, the better the fix. And when something does work, reinforce it with a note like this.
This works correctly. Use this version going forward. That way, the model sticks to good code. One last tip here, protect your progress. Sometimes fixing one bug breaks something else. I always keep a clean branch or backup repo or sometimes just a copy a file with working code. If things go sideways, I can roll back past and just keep moving. This workflow turns coding with AI into a real collaboration. You guide it with context, keep it align with the plan and reinforce the working pieces as you go.
After a few iterations, you will have a working prototype. Once you get that MVP running, deployment is very simple. All you need is just this one command and then app will be redeployed. Now we can test the full flow. Upload the document, generate questions, save them as a quiz and then practice. And also try to query lakebase and see that all your attempts are saved and also all of the generated questions are in database so that you can reuse them.
That's a moment when VIP coding really pays off. You've got from an idea to a working app in a fraction of time that it would normally take. And as you prepare to share this app with your team, you can set up different authorization options. Our app currently uses service principle permissions, but you can also enable user authorization where each user's existing Unity catalog permissions will automatically apply. For example, if a table limits US users from seeing European data, the app automatically enforces this restrictions.
You can see this controls under the user authorization section. So, let's recap what we just accomplished. We went from idea to a working app. We used lake base for data data bricks apps for runtime, powered it with clothes onet 4 and utilized wipe coding in our favorite ID. But here is the real win. We did this without sacrificing enterprise security or creating data cells. Everything runs inside our existing governance model.

12:20 Wrap-Up & Next Steps

And this is just the beginning. You could swap out the starter URL for something more polished or integrate delta tables so your app generates questions directly from your enterprise data instead of PDFs. Let me know if you would like to see this as part two. You will find this examples in datab bricks cookbook and I will leave all of this links in the descriptions of this video. And if you build something cool with this tag, please drop a comment.
I really would love to see what you create. Thanks for watching and happy pipe coding.

Learn more about the Databricks Data and AI platform.

The information provided herein is for general informational purposes only and may not reflect the most current product capabilities or configurations.