Workflow overview
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Helpful for business development and pipeline building.
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."
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