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Query Databricks data and SQL insights via Slack with Gemini AI agent

n8nn8n11 modulesv1.0
SlackGemini

Automated Databricks Data Querying & SQL Insights via Slack with AI Agent & Gemini --- Node-by-Node Explanation This workflow is divided into three functional phases: Initialization, AI Processing, and Response Delivery. | Node Name | Category | What it does | | :--- | :--- | :--- | | When Slack Message Received | Trigger | Monitors a Slack channel for @mentions. It captures the user's question and the thread ID to keep the conversation organized. | | Set Data

At a glance

Query Databricks data and SQL insights via Slack with Gemini AI agent is a ready-made n8n workflow you import as a workflow JSON file — no build required. It connects Slack, Gemini. It's free to download. Follow the 5-step import below to go live in minutes.

Platform
n8n
Connects
Slack, Gemini
Modules
11
Price
Free
Version
v1.0
Query Databricks data and SQL insights via Slack with Gemini AI agent workflow diagram

About this workflow

Automated Databricks Data Querying & SQL Insights via Slack with AI Agent & Gemini --- Node-by-Node Explanation This workflow is divided into three functional phases: Initialization, AI Processing, and Response Delivery. | Node Name | Category | What it does | | :--- | :--- | :--- | | When Slack Message Received | Trigger | Monitors a Slack channel for @mentions. It captures the user's question and the thread ID to keep the conversation organized. | | Set Databricks Config | Configuration | A "helper" node where you hardcode your Databricks warehouseid and targettable. This makes it easy to update settings in one place. | | Fetch Databricks Schema | Data Retrieval | Sends a DESCRIBE command to the Databricks API. It learns what columns exist (e.g., "price", "date", "storeid") so the AI knows what it can query. | | Parse Table Schema | Data Transformation | Uses JavaScript to clean up the raw Databricks response. it converts complex technical data into a simple list that the AI can easily read. | | SQL Data Analyst Agent | AI Brain | The "manager" of the workflow. It takes the user's question and the table schema, decides which SQL query to write, and interprets the results. | | Gemini Model | LLM Engine | Provides the actual intelligence (using Google Gemini 3.1 Flash). This is what "thinks" and generates the SQL and conversational text. | | Redis Chat Memory | Memory | Stores previous messages in the thread. This allows you to ask follow-up questions (e.g., "Now show me only the top 5") without repeating the whole context. | | Run Primary SQL Query | AI Tool | An HTTP tool given to the Agent. The Agent "calls" this node to actually run the generated SQL on Databricks and get the real data back. | | If Output Valid | Logic Gate | A safety check. It verifies if the Agent successfully produced a message for Slack or if something went wrong during the process. | | Post to Slack Channel | Output (Success) | Sends the final answer (e.g., "The total revenue for Q3 was $4.2M") back to the user in Slack. | | Post Error to Slack | Output (Failure) | If the SQL fails or the AI hits a wall, this node sends an error message to the user so they aren't left waiting. | --- How the "Agent" Loop Works Unlike a standard linear workflow, the SQL Data Analyst Agent doesn't just move to the next step. It performs a "Reasoning" loop: 1. Observe: "The user wants to know sales for March." 2. Think: "I have a table called 'franchises' with a 'saledate' column. I should run a SUM query." 3. Act: It triggers the Run Primary SQL Query node. 4. Observe Results: "The query returned 150,000." 5. Final Response: "The total sales for March were 150,000."

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How to import this n8n workflow

  1. 1

    Download the workflow JSON file after purchase.

  2. 2

    Open n8n → click the menu → Import from File.

  3. 3

    Select the downloaded JSON and import.

  4. 4

    Set up credentials for each node that requires them.

  5. 5

    Click Execute Workflow to test, then activate.

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