using databricks sql warehouse as web app backend

[AhsanKhawaja]

Hi,

I wanted to ask if anyone is using Databricks SQL Warehouse as backend for small to large scale web application?

What are your thoughts about it, specially what Databricks team thinks of it ?

Kind Regards,

A

[-werners-]

it is possible but:

• you need to have a cluster running all the time. Sql serverless spins up fast, but it takes several seconds nonetheless which can be too long of a wait for a web app.
• calculate the cost. It might be cheaper to run a classic RDBMS
• think about optimizations that might be necessary for your app (indexing mainly) that are not always possible in databricks
• do not forget labour cost for maintenance/development (this is probably a pro for databricks)

I might think of more things later.

[Hubert-Dudek]

Also, you need to provide more details. If you want to load for user report with 1000 rows is excellent. If you want to load the last news for your webpage, you need something with low latency.

[stefnhuy]

Hey AhsanKhawaja,

Yeah, I've played around with using Databricks SQL Warehouse as a backend for a web app. It's got its perks, but like anything, it depends on your needs.

From my experience, it's slick for handling big data and crunching numbers. If your web app deals with heavy analytics or large datasets, it could be a solid choice. The SQL integration is smooth, and the performance is decent.

Speaking of trends in the tech space, have you checked out the latest insights on mobile app development? I recently came across an interesting article titled 'Mobile Apps Future: Unveiling the Top Trends Shaping the Next Generation' It delves into the evolving landscape of mobile apps and could offer some valuable insights for your considerations.

As for what the Databricks team thinks, well, they're obviously on board with their own product. They'll highlight the scalability and analytics prowess. But remember, every tool has its sweet spot.

Just a heads-up: keep an eye on costs as you scale. Sometimes, those bills can surprise you. Also, ensure your team is comfortable with the learning curve.

Give it a spin in a smaller project first, see how it fits.

[Bobbiejbrigs]

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[Robert-Scott]

Using Databricks SQL Warehouse as a backend for a web application involves integrating Databricks with your web app to handle data processing, querying, and analytics. Here are the steps to achieve this:

1. Set Up Databricks SQL Warehouse

1. Create a Databricks Account: If you don't already have a Databricks account, sign up for one.
2. Create a Cluster: Set up a Databricks cluster to run SQL queries. You can do this from the Databricks workspace by navigating to the "Clusters" section and creating a new cluster.
3. Set Up SQL Warehouse: Go to the "SQL" tab in the Databricks workspace and create a new SQL warehouse. This will be used to execute your SQL queries.

2. Connect to Databricks SQL Warehouse

1. Get Connection Details: Obtain the JDBC/ODBC connection details from the Databricks workspace. You can find these details in the "SQL Endpoints" section.
2. Install JDBC/ODBC Drivers: Ensure that your web application environment has the necessary JDBC or ODBC drivers installed to connect to Databricks. You can download these from the Databricks website.

3. Develop Your Web Application Backend

1. Choose Your Web Framework: Select a web framework for your application backend (e.g., Flask or Django for Python, Express for Node.js, Spring Boot for Java).

2. Establish a Connection to Databricks:

   • For Python, you might use a library like pyodbc or sqlalchemy.
   • For Node.js, you could use the odbc package.
   • For Java, you can use JDBC.

3. Execute Queries: Write functions in your backend to execute SQL queries against the Databricks SQL warehouse. Here is an example using Python and SQLAlchemy:

   python

   Copy code

   from sqlalchemy import create_engine from sqlalchemy.engine import URL # Connection details databricks_url = URL.create( "databricks", username="YOUR_USERNAME", password="YOUR_PASSWORD", host="YOUR_HOST", port="YOUR_PORT", database="YOUR_DATABASE", query={"HTTPPath": "YOUR_HTTP_PATH"} ) # Create an engine and connect to Databricks engine = create_engine(databricks_url) # Example function to execute a query def execute_query(query😞 with engine.connect() as connection: result = connection.execute(query) return result.fetchall() # Example query query = "SELECT * FROM your_table" results = execute_query(query) print(results)

4. Integrate with Frontend

1. Create API Endpoints: Develop API endpoints in your backend to handle requests from your web application's frontend. These endpoints will call the functions that execute queries on the Databricks SQL warehouse.
2. Handle Data Requests: Implement data fetching and manipulation logic in your frontend to interact with these API endpoints.

5. Deploy Your Application

1. Deploy Backend: Host your backend on a platform like AWS, Azure, Google Cloud, or any other cloud service provider.
2. Deploy Frontend: Host your frontend on a web server or use a platform like Vercel or Netlify.
3. Ensure Security: Secure your connection to Databricks using appropriate authentication mechanisms and encrypt sensitive data.

By following these steps, you can effectively use Databricks SQL Warehouse as the backend for your web application, enabling powerful data processing and analytics capabilities.

[nailapatial]

Integrating Databricks SQL Warehouse as the backend for a web application involves several steps to handle data processing, querying, and analytics. Here's a simplified guide:

1. Set Up Databricks SQL Warehouse

   • Create an Account: Sign up for a Databricks account if you don’t have one.
   • Set Up a Cluster: Create a Databricks cluster from the workspace to run SQL queries.
   • Create SQL Warehouse: In the Databricks workspace, go to the "SQL" section and set up a SQL warehouse for executing queries.

2. Connect to Databricks SQL Warehouse

   • Get Connection Details: Obtain JDBC/ODBC connection info from the Databricks workspace.
   • Install Drivers: Ensure your web app environment has the necessary JDBC or ODBC drivers installed.

3. Develop Your Backend

   • Select a Web Framework: Choose a framework like Flask (Python), Express (Node.js), or Spring Boot (Java).
   • Establish Connection: Use libraries like SQLAlchemy (Python) or JDBC (Java) to connect to Databricks.
   • Execute Queries: Write functions in your backend to run SQL queries against the SQL warehouse.

4. Integrate with Frontend

   • Create API Endpoints: Develop endpoints in your backend for your frontend to interact with Databricks.
   • Handle Data Requests: Implement logic in your frontend to fetch and manipulate data via these endpoints.

5. Deploy Your Application

   • Deploy Backend and Frontend: Host your backend and frontend using platforms like AWS, Azure, or Vercel.
   • Ensure Security: Secure your connections to Databricks with proper authentication and data encryption.

By following these steps, you can effectively use Databricks SQL Warehouse to power your web application’s data processing and analytics needs.

[sfdsfdfgdggd]

I’ve experimented with using Databricks SQL Warehouse as the backend for a web app, and it has its advantages, but like any tool, it depends on your specific requirements.

From my experience, it’s great for handling large datasets and performing heavy analytics. If your web app involves complex data analysis or big data, Databricks could be a solid choice. The SQL integration is smooth, and performance is generally strong.

On another note, have you checked out the latest trends in mobile app development? I came across an insightful article called 'Mobile Apps Future: Unveiling the Top Trends Shaping the Next Generation'. It covers the shifting landscape of mobile apps and could provide some useful perspectives for your project.

As for Databricks’ own view, they’ll obviously promote its scalability and analytics capabilities. But like with any tool, it’s important to recognize its best use cases.

One thing to keep in mind: be cautious of costs as you scale up. Sometimes the pricing can be a bit of a surprise. Also, the learning curve might be steep for your team, so make sure they’re comfortable with it.

I recommend testing it out on a smaller project first to see how well it aligns with your needs.