• DATA
  • SCHEMA
  • LOGIC
  • HUMAN

How to deploy streaming SQL pipelines with your AI

Connect Materialize to your agent to turn natural language into live, streaming data views. Once connected, your agent handles the SQL execution for you.

Ask AI about this page

Short answer

Can my AI build streaming views in Materialize?

Yes. Your agent uses SQL to create new data sources and materialized views. You get real-time data updates by describing the logic you want to see.

The outcomes.

What your agent delivers.

The agent handles the heavy lifting of syntax and execution.

DATA

Live materialized views

The agent executes SQL to build views that update as new data arrives. You get a continuous stream of fresh information.

SCHEMA

New data sources

Your agent creates the underlying structures needed for your pipelines. It sets up the tables and sources you need to query.

LOGIC

Complex SQL transformations

The agent writes the join and aggregation logic for you. It turns raw event streams into structured, queryable datasets.

HUMAN

Review and approval

You decide which SQL commands are safe to run. The agent proposes the code, but you maintain control over the database state.

The workflow

What your AI does when the request arrives.

The agent translates your intent into valid SQL commands.

  1. Analyze requirements

    The agent looks at your existing schema and the data you want to transform. It determines the necessary SQL structure.

    execute_sql

  2. Draft SQL commands

    The agent writes the specific CREATE or SELECT statements needed for your materialized view. It handles the syntax for streaming logic.

    execute_sql

  3. Apply changes

    The agent sends the commands to Materialize to build the new views or sources. This makes the data immediately available.

    execute_sql

  4. Verify results

    The agent runs queries to check that the new data source or view contains the expected information. It confirms the pipeline is live.

    execute_sql

Try it

Copy these to start.

Use these prompts to kick off your first pipeline.

Starting points

Swap out the table names and column descriptions to match your specific database schema.

Materialize Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_b3YATqNUmev2sLGakCv9DXqsXBiI2C8N6xSKsiWQ/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Materialize capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "materialize-streaming-sql-db-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_b3YATqNUmev2sLGakCv9DXqsXBiI2C8N6xSKsiWQ/mcp"
    }
  }
}
Copy into chat04

  • Create a materialized view from the user_events table that shows real-time click counts per hour.

  • Build a new data source by joining the orders and customers tables.

  • Write a SQL command to create a view that aggregates total revenue by region from the sales stream.

  • Check the current schema of my materialized views to see if the sensor_data source is active.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect Materialize once, then ask.

Just link your account once. Your credentials stay encrypted, and you can immediately start building pipelines through your agent.

Connect Materialize to your AI

FAQ

How this task behaves.

  • 01

    Can the AI delete my existing tables?

    No. The agent only has the tools to execute SQL commands you authorize. It cannot perform destructive operations unless you specifically instruct it to do so.

  • 02

    Does the AI manage my Materialize billing?

    No. The agent only interacts with the database via SQL. All account and billing management happens directly in Materialize.

  • 03

    Will the agent fix my broken SQL syntax?

    Yes. If a command fails, the agent can see the error and rewrite the SQL to fix the mistake.

  • 04

    Can the AI create new databases?

    No. The agent is limited to the database context it is connected to via the execute_sql tool.

  • 05

    How does the agent know my table names?

    The agent uses the connection to read your schema so it knows which tables and columns are available to query.

  • More questions about Materialize? The Connector page answers them. See everything the Materialize Connector can do

Connect Materialize to Claude, Cursor, ChatGPT & more