Create a QuestDB Table and Insert Data

Overview

This workflow automates the process of creating a new table in QuestDB and inserting a single row of data into it. It is triggered manually, making it ideal for testing database operations or bootstrapping a schema before integrating with other automated pipelines. The workflow demonstrates how to execute raw SQL statements and then perform an insert using n8n's QuestDB node.

Workflow Steps

  1. On clicking 'execute' (Manual Trigger) — No auth required. This node starts the workflow when you click the "Execute Workflow" button in the n8n editor. It is useful for testing and one-off operations.

  2. QuestDB (QuestDB node, operation: executeQuery) — Database credentials required (host, port, username, password). This node runs the SQL statement CREATE TABLE test (id INT, name STRING); to create a new table named test with two columns: an integer id and a string name. The alwaysOutputData option is enabled, so the node always passes data downstream even if the query returns no rows.

  3. Set (Set node) — No auth required. This node prepares the data to be inserted. It sets two fields:

    • id (number) — The value is left empty in the configuration (likely intended to be filled dynamically or from previous data).
    • name (string) — Set to the static value "Tanay". The output of this node becomes the input for the next QuestDB node.
  4. QuestDB1 (QuestDB node, operation: insert) — Same database credentials as the first QuestDB node. This node inserts the data from the Set node into the test table. The table parameter is set to "test" and the columns parameter is "id, name". The actual values come from the incoming data (the id and name fields set by the previous node).

Setup Instructions

  1. QuestDB instance — You need a running QuestDB server. You can install it locally or use a cloud instance. Obtain the host, port (default 8812 for PostgreSQL wire protocol), username, and password.
  2. n8n credentials — In n8n, create a new QuestDB credential. Enter the host, port, database name (if any), username, and password. The same credential will be used by both QuestDB nodes.
  3. Manual trigger — No additional setup required. Just click the "Execute Workflow" button in the editor to run the workflow.

Use Cases & Variations

  • Schema initialization — Use this workflow as a template to create tables before running data ingestion pipelines.
  • Dynamic data insertion — Replace the Set node with an HTTP Request node (e.g., from a webhook or API) to insert data from external sources.
  • Batch inserts — Modify the workflow to loop over an array of records and insert multiple rows using the QuestDB node's insert operation.
  • Error handling — Add an error trigger or conditional logic to handle cases where the table already exists (e.g., use CREATE TABLE IF NOT EXISTS).
  • Scheduled execution — Replace the manual trigger with a Schedule Trigger to run the workflow at regular intervals (e.g., daily data refresh).
4 nodesmanual triggerData
SetQuest Db

Workflow JSON

{
  "id": "161",
  "name": "Create a table and insert data into it",
  "nodes": [
    {
      "name": "On clicking 'execute'",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        440,
        460
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "name": "Set",
      "type": "n8n-nodes-base.set",
      "position": [
        840,
        460
      ],
      "parameters": {
        "values": {
          "number": [
            {
              "name": "id"
            }
          ],
          "string": [
            {
              "name": "name",
              "value": "Tanay"
            }
          ]
        },
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "name": "QuestDB",
      "type": "n8n-nodes-base.questDb",
      "position": [
        640,
        460
      ],
      "parameters": {
        "query": "CREATE TABLE test (id INT, name STRING);",
        "operation": "executeQuery"
      },
// ... truncated (copy to see full JSON)

How to Import This Workflow

  1. 1Copy the workflow JSON above using the Copy Workflow JSON button.
  2. 2Open your n8n instance and go to Workflows.
  3. 3Click Import from JSON and paste the copied workflow.

Don't have an n8n instance? Start your free trial at n8nautomation.cloud

Related Templates

AI-Powered CV Scanner with Google Sheets & Gemini

Overview This workflow automates the initial screening of job applicants by combining a web form, AI-powered CV analysis, and Google Sheets. When a candidate submits their application (name, email, and CV PDF), the workflow instantly extracts the text from the PDF using Mistral OCR, then sends it to Google Gemini for evaluation against a predefined job description. The AI returns a qualification score (0.0 to 1.0) and a detailed explanation, which are then logged alongside the candidate's details in a Google Sheet. This eliminates manual CV review, speeds up your hiring pipeline, and ensures every applicant is evaluated consistently. Workflow Steps Application Form (Form Trigger) — No auth required. This node presents a custom-branded web form to collect the candidate's Full Name, Email, and CV (PDF upload). The form is styled with a dark, glassmorphism theme and includes animated backgrounds. Once submitted, the workflow triggers automatically. Log Candidate Submission (Google Sheets) — OAuth2. Immediately after form submission, this node appends the candidate's name and email to the 'CVs' Google Sheet. This ensures the application is recorded even if the subsequent analysis steps fail. Extract CV Text (Mistral AI) — API Key auth. This node takes the uploaded PDF file from the form and uses Mistral's OCR API to extract all text content from the CV. It dynamically selects the binary file uploaded in the previous step. AI Qualification (LLM Chain) — This is the core analysis node. It uses a detailed prompt that includes: - A system message defining the AI as a professional, objective hiring assistant. - The full job description for a "Senior Frontend Developer" with core requirements and preferred qualifications. - Clear evaluation logic: candidates missing core requirements cannot score above 0.6; meeting all core requirements yields at least 0.75; preferred qualifications add bonuses up to 1.0. - The extracted CV text as context. - An instruction to output raw JSON only. Gemini 2.5 Flash Lite (Language Model) — API Key auth (Google AI Studio). This node provides the actual AI model (Google Gemini 2.5 Flash Lite) with a temperature of 0.4 for balanced, deterministic outputs. It powers the LLM Chain above. JSON Output Parser (Output Parser) — This node ensures the AI's response is valid JSON with the exact schema: (number) and (string). It prevents malformed responses from breaking downstream nodes. Add CV Analysis (Google Sheets) — OAuth2. After analysis, this node updates the candidate's row in the 'CVs' sheet by matching on the Email column. It writes the and from the AI's output into the corresponding columns. Create 'CVs' Spreadsheet (Google Sheets) — OAuth2. This node runs once when the workflow is first triggered manually. It creates a new Google Sheet named 'CVs' with columns: FullName, Email, QualificationRate, QualificationDescription. Setup Instructions Prerequisites A Google account (for Google Sheets) A Mistral AI account (free tier available) — Get your API key here A Google AI Studio account — Create an API key here Step-by-Step Google Sheets Credentials: In the three Google Sheets nodes, create or select an OAuth2 credential for Google Sheets. Grant access to create and edit spreadsheets. Mistral AI Credential: In the "Extract CV Text" node, create a new credential and paste your Mistral API key. Gemini Credential: In the "Gemini 2.5 Flash Lite" node, create a new credential and paste your Google AI Studio API key. Customize Job Description: Open the "AI Qualification" node and edit the section in the prompt to match your actual job posting. Activate the Workflow: Toggle the workflow to "Active" status. The "Application Form" node will generate a public URL you can share with candidates. Test: Use the "Start Here" manual trigger to initialize the Google Sheet, then submit a test application via the form URL. Use Cases & Variations Custom Job Roles: Replace the Senior Frontend Developer description with any role (e.g., Marketing Manager, Data Scientist, Sales Rep) by editing the prompt in the AI Qualification node. Different Storage: Replace Google Sheets with Airtable, Notion, or a database like PostgreSQL for storing applications and results. Multi-Round Screening: Add a Slack or email notification node to alert the hiring team when a candidate scores above a certain threshold (e.g., >0.8). Interview Scheduling: Connect the output to a Calendly or Google Calendar node to automatically invite high-scoring candidates for an interview. Batch Processing: Modify the trigger to accept a CSV upload of multiple CVs for bulk analysis. Enhanced Analysis: Add more output fields like "years of experience", "top skills", or "red flags" by updating the JSON schema in the Output Parser and the prompt.

18 nodes

Talk to Your Google Sheets Data Using OpenAI Chat Agent

This workflow transforms a Google Sheet into a live, conversational database. Using an AI agent powered by OpenAI's GPT-4.1-nano model, you can ask natural language questions about your spreadsheet data and receive accurate, context-aware answers — without writing any SQL or formulas. The agent can handle queries like total campaign spend, per-channel breakdowns, month-over-month trends, and cost-per-lead calculations. Workflow Steps Node-by-Node Breakdown Chat with Your Data (Chat Trigger) — This is the entry point of the workflow. It provides a chat interface (webhook) where users type their questions. No authentication is required to trigger the webhook; access can be secured at the workflow level. Memory (Buffer Window Memory) — Stores recent conversation history so the AI can reference previous exchanges. The buffer window keeps the last few messages to maintain context without excessive token use. No authentication required. OpenAI Chat Model (OpenAI GPT-4.1-nano) — The language model that processes user questions and generates answers. Uses API Key auth (you must provide an OpenAI API key). Parameters: model = , no additional options configured. Talk to Your Data (AI Agent) — The core agent that orchestrates the conversation. It receives user input, decides which tool to call (only the Google Sheets tool is available), and returns answers. Uses API Key auth (uses the same OpenAI credential as the model). Parameters include a system message: "Google Sheets Ask-… You are Ask-… Answer questions using Google Sheets ONLY via the tool below. Be precise and conservative. There is only one dataset. Don't ask what dataset it is. Use the data tool to answer the question." Analyze Data (Google Sheets Tool) — The tool the agent calls to read spreadsheet data. Uses OAuth2 authentication (Google Sheets). Parameters: document ID = ("Sample Marketing Data - n8n"), sheet name = "Data" (gid: 365710158). No additional options set. Sticky Notes (Information Only) — Provide setup instructions and tips. Not executable nodes. Setup Instructions Before using this workflow, you need: OpenAI Account — Visit OpenAI API Keys to generate an API key. Make sure you have billing set up at OpenAI Billing and have credits available. Google Account — You need access to a Google Sheet. The sample uses a specific sheet, but you can point it to any sheet with a similar structure: first row = column headers, rows 2–100 = data. To configure: In the OpenAI Chat Model node, create an OpenAI credential with your API key. In the Analyze Data node, create a Google Sheets OAuth2 credential (select your Google account and authorize access). Update the Document ID and Sheet Name to match your own Google Sheet. Optionally update the system message in the Talk to Your Data agent if you want to change the assistant's personality or instructions. Use Cases and Variations This workflow is perfect for: Marketing teams wanting instant answers about campaign performance, spend, and ROI. Sales operations analyzing pipeline data, deal stages, or conversion metrics. Small business owners asking questions about inventory, sales, or customer data without learning SQL. Adaptations: Replace Google Sheets with another data source tool (e.g., Airtable, Notion, PostgreSQL, MySQL) by swapping the "Analyze Data" node. Add more tools to the agent (e.g., a web search tool, a calculator) to expand its capabilities. Change the model to GPT-4o or GPT-3.5-turbo for faster/cheaper responses. Add a Slack or Webhook trigger instead of the chat trigger for a different user interface.

11 nodes

Extract company website metadata via Apify Company Enrichment

This workflow takes a single company domain (e.g., ), validates it, runs it through Apify's Company Enrichment actor, and returns a verified metadata result with provenance. It is designed as a building block for CRM enrichment, lead generation, or data quality pipelines. Workflow Steps Run manually (Manual Trigger) – Starts the workflow on demand. No schedule is set; you can extend it with a cron trigger. Set company domain (Set node) – Hardcodes the domain into a field . Change this to your target domain. Validate domain (Code node, JavaScript) – Validates the domain format (length, allowed characters, no protocol or path). Throws an error if invalid. Also attaches an marker for downstream validation. Extract metadata on Apify (HTTP Request, Header Auth) – Sends a POST request to the Apify actor's sync endpoint with the domain payload. Uses Header Auth with a custom header . Parameters: , to cap cost. Timeout 150 seconds. Validate metadata result (Code node, JavaScript) – Checks the response: only one item, , domain matches, integration source matches, and provenance method is . Rejects any unexpected or empty results. Setup Instructions Get an Apify API token – Sign up or log in at apify.com. Create a token scoped to run actors (). Create an n8n Header Auth credential – In n8n, go to Credentials > New > Header Auth. Set the header name to and value to . Attach this credential to the Extract metadata on Apify node. Replace the default domain – In the Set company domain node, change to your target company domain (e.g., ). Review pricing – The actor charges per run. The workflow caps cost at $0.02 USD per call. Review actor pricing before scaling. Run manually – Click Execute Workflow to test. The output will be a single enriched company object. Use Cases & Adaptations CRM integration – Pipe the output into Salesforce or HubSpot via an HTTP Request or their respective n8n nodes. Bulk enrichment – Replace the manual trigger with a Schedule Trigger or a Spreadsheet File Trigger to process thousands of domains from a Google sheet or CSV. Data validation – Use the validated result to enrich lead lists, verify company websites, or update a database. Custom enrichment – Replace the Apify actor with another REST API (e.g, Clearbit, Zoominfo) by modifying the HTTP Request node and validation logic.

6 nodes

Ready to automate with n8n?

Get affordable managed n8n hosting with 24/7 support.