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

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1. Workflow Overview

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 , a...

Best for

Document Extraction automation workflows
AI Chatbot automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.stickynote, n8n-nodes-base.slacktrigger, n8n-nodes-base.code, n8n-nodes-base.slack, n8n-nodes-base.httprequest, n8n-nodes-base.set, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatgooglegemini

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by iamvaar.

Original n8n.io source

1.1 Workflow description

Title: Query Databricks data and SQL insights via Slack with Gemini AI agent
Workflow name: Query Databricks data and SQL insights via Slack with Gemini AI agent

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 `warehouse_id` and `target_table`. 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", "store_id") 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:

Observe: "The user wants to know sales for March."
Think: "I have a table called 'franchises' with a 'sale_date' column. I should run a SUM query."
Act: It triggers the Run Primary SQL Query node.
Observe Results: "The query returned 150,000."
Final Response: "The total sales for March were 150,000."

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 2 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 3 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 4 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 5 - When Slack Message Received

Type / Role: n8n-nodes-base.slackTrigger - slackTrigger
Config choices: Version 1

Block 6 - Parse Table Schema

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 7 - Post to Slack Channel

Type / Role: n8n-nodes-base.slack - slack
Config choices: Version 2.4

Block 8 - Fetch Databricks Schema

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.4

Block 9 - Set Databricks Config

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 10 - SQL Data Analyst Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 3.1

Block 11 - Gemini Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 12 - Redis Chat Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryRedisChat - memoryRedisChat
Config choices: Version 1.5

Block 13 - Run Primary SQL Query

Type / Role: n8n-nodes-base.httpRequestTool - httpRequestTool
Config choices: Version 4.4

Block 14 - If Output Valid

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.3

Block 15 - Post Error to Slack

Type / Role: n8n-nodes-base.slack - slack
Config choices: Version 2.4

3. Summary Table

Workflow	Query Databricks data and SQL insights via Slack with Gemini AI agent
Complexity	advanced
Nodes	15
Categories	Document Extraction, AI Chatbot
Author	iamvaar
Published	23 Mar 2026

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/14254/14254.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Document Extraction workflows AI Chatbot workflows

Frequently asked questions

What does Query Databricks data and SQL insights via Slack with Gemini AI agent do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI Chatbot use case.

iamvaar

38 workflows

Nodes

n8n-nodes-base.stickynote n8n-nodes-base.slacktrigger n8n-nodes-base.code n8n-nodes-base.slack n8n-nodes-base.httprequest n8n-nodes-base.set @n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.lmchatgooglegemini

Complexity

advanced

Published 23 Mar 2026

Likes 0

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Install path: /data/workflows/14254/14254.json

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

Workflow preview

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Automated Databricks Data Querying & SQL Insights via Slack with AI Agent & Gemini

Node-by-Node Explanation

How the "Agent" Loop Works

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Sticky Note

Block 2 - Sticky Note1

Block 3 - Sticky Note2

Block 4 - Sticky Note3

Block 5 - When Slack Message Received

Block 6 - Parse Table Schema

Block 7 - Post to Slack Channel

Block 8 - Fetch Databricks Schema

Block 9 - Set Databricks Config

Block 10 - SQL Data Analyst Agent

Block 11 - Gemini Model

Block 12 - Redis Chat Memory

Block 13 - Run Primary SQL Query

Block 14 - If Output Valid

Block 15 - Post Error to Slack

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions