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Data processing and analytics

Automate Barcode Reading from Google Drive PDFs to Google Sheets
Streamline your workflow by automatically reading barcodes from PDFs stored in Google Drive and logging the results in Google Sheets using PDF.co's powerful API.
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Automatically Transfer Amplitude Chart Data to Google Sheets
This workflow regularly retrieves chart data from Amplitude and inserts it into a specified Google Sheets spreadsheet, ensuring your data is always up-to-date and easily accessible.
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Sync ActiveCampaign analytics to Google Sheets
Automatically sync your ActiveCampaign analytics to Google Sheets to enhance your marketing campaigns. his template transfers key performance metrics every Friday, allowing you to access fresh insights at the start of each week. Optimize your strategies with up-to-date data, reduce manual tasks, and improve decision-making efficiency. Ideal for teams looking for a streamlined approach to campaign tracking and analysis. he scenario runs every Friday (feel free to change the schedule if needed). You can change this in the scheduling settings by clicking the clock icon.

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Automatically Save Koncile Processed Data to Google Drive

Streamline your document processing by using Koncile to extract structured data and automatically save it as a CSV in Google Drive. This workflow eliminates manual exports, making it ideal for archiving and reporting.

Automate Google Sheets Updates with ChatGPT Insights on Cell Changes

Automatically update Google Sheets rows with insights generated by ChatGPT whenever a cell change is detected, streamlining data analysis and enhancing decision-making.

Export all messages from dialogs in Chatforma

Export all messages from all dialogs from Chatforma to the specified Google Sheets spreadsheet. he template uses this [example spreadsheet](https://docs.google.com/spreadsheets/d/1mQAdPmQqNvL8Qp97ZmVm0wbeI17PR0vcU7l33lUtbM/edit?usp=sharing).

Automatically Transfer Unsubscribed Contacts from Autopilot to Databox

Effortlessly send data of contacts who unsubscribe from Autopilot to Databox for comprehensive analysis.

Automate Record Search and Deletion in Knack Database

Efficiently manage your Knack database by automating the search and deletion of specific records. This workflow streamlines data maintenance by using filters to identify records and remove them automatically.

Automate Airtable Record Creation from Typeform Responses

Effortlessly manage your data by automatically creating Airtable records for each new Typeform response. This workflow streamlines data entry, minimizes errors, and ensures your information is organized for easy analysis.

Automate Exporting QuestionPro Survey Responses to Google Sheets

Automatically export your QuestionPro survey responses to Google Sheets at regular intervals, ensuring your data is always up-to-date and easily accessible.

Automate Article Extraction from RSS to Google Cloud Firestore

Effortlessly extract new articles from RSS feeds, convert HTML content to plain text, and store them in Google Cloud Firestore for streamlined access and management.

Deduplicate Data Records Using JavaScript Array Methods

## How It Works - Data Deduplication in n8n This tutorial demonstrates how to remove duplicate records from a dataset using JavaScript logic inside n8n's Code nodes. It simulates real-world data cleaning by generating sample user data with intentional duplicates (based on email addresses) and walks you through the process of deduplication step-by-step. **The process includes**: - Creating Sample Data with duplicates. - Filtering Out Duplicates using filter() and findIndex() based on email. - Displaying Cleaned Results with simple statistics for before-and-after comparison. This is ideal for scenarios like CRM imports, ETL processes, and general data hygiene. ## 🏗 Set-Up Steps ✅ Step 1: Manual Trigger Node: When clicking "Test workflow" Purpose: Initiates the workflow manually for testing. ✅ Step 2: Generate Sample Data Node: Create Sample Data (Code node) What it does: - Creates 6 users, including 2 intentional duplicates (by email). - Outputs data as usersJson with metadata (totalCount, message). - Mimics real-world messy datasets. ✅ Step 3: Deduplicate the Data Node: Deduplicate Users (Code node) What it does: - Parses usersJson. - Uses .filter() + .findIndex() to keep only the first instance of each email. - Logs total, unique, and removed counts. - Outputs clean user list as separate items. ✅ Step 4: Display Results Node: Display Results (Code node) What it does: **Outputs structured summary**: - Unique users - Status - Timestamp Prepares results for review or downstream use. ### Sample Output - Original count: 6 users - Deduplicated count: 4 users - Duplicates removed: 2 users 🎯 Learning Objectives **You'll learn how to**: - Use .filter() and .findIndex() in n8n Code nodes - Clean JSON data within workflows - Create simple, effective deduplication pipelines - Output structured summaries for reporting or integration **Best Practices** - Validate input format (e.g., JSON schema) - Handle null or missing fields gracefully - Use logging for visibility - Add error handling for production use - Use pagination/chunking for large datasets

Extract Emails from Website HTMLs

## How to Scrap Emails from Websites This workflow shows how to quickly build an Email scraping API using n8n. Email marketing is at the core of most marketing strategies, be it content marketing, sales, etc. As such, being able to find contacts in bulk for your business on a large scale is key. There are available tools available in the market that can do this, but most are premium; why not build a custom one with n8n? ## Usage The workflow gets the data from a website and performs an extraction based on the data around on the website. 1. Copy the webhook URL to your browser. 2. Add a query parameter e.g., ?Website=https://mailsafi.com. This should give you a URL like this {{$n8nhostingurl/webhook/ea568868-5770-4b2a-8893-700b344c995e?Website=https://mailsafi.com}}. 3. Click on the URL and wait for the extracted email to be displayed. This will return the email address on the website, or if there is no email, the response will be workflow successfully executed. **Make sure to use HTTP:// for your domains** Otherwise, you may get an error.

Enrich Up to 1500 Emails per Hour with Dropcontact Batch Requests

The template allows making Dropcontact batch requests up to 250 requests every 10 minutes (1500/hour). Valuable if high volume email enrichment is expected. Dropcontact will look for email & basic email qualification if first_name, last_name, company_name is provided. --- **Step 1: Node Profiles Query** Connect your own source (Airtable, Google Sheets, Supabase,...); the template is using Postgres by default. **Note I:** Be careful your source is only returning a maximum of 250 items. **Note II:** The next node uses the following variables, make sure you can map these from your source file: - first_name - last_name - website (company_name would work too) - full_name (see note) **Note III:** This template is using the Dropcontact Batch API, which works in a POST & GET setup. Not a GET request only to retrieve data, as Dropcontact needs to process the batch data load properly. --- **Step 2: Node Data Transformation** Will transform the input variables into the proper JSON format. This JSON format is expected from the Dropcontact API to make a batch request. full_name is being used as a custom identifier to update the returned email to the proper contact in your source database. To make things easy, use a unique identifier in the full_name variable. --- **Step 3: Node: Bulk Dropcontact Requests.** Enter your Dropcontact credentials in the node: Bulk Dropcontact Requests. --- **Step 4: Connect your output source by mapping the data you like to use.** --- **Step 5: Node: Slack (OPTIONAL)** Connect your Slack account, if an error occurs, you will be notified. **TIP:** Try to run the workflow with a batch of 10 (not 250) as it might need to run initially before you will be able to map the data to your final destination. Once the data fields are properly mapped, adjust back to 250.

Automate Qualtrics Contact Creation from Google Sheets

Automatically create Qualtrics directory contacts from new entries in a Google Sheets spreadsheet, streamlining data management and ensuring up-to-date contact information.

Add Google Sheets rows for new Amazon SES contacts

Make will watch your Amazon SES contacts and add them to your Google Sheets as rows. Heres the [spreadsheet](https://docs.google.com/spreadsheets/d/1FeaALMYAUmUlQssrMVCIyvojkX5V9tpNXgo9rS_9egA/edit?usp=sharing) used in this scenario.

Parse Y Combinator News Page

Extract data from a webpage (Ycombinator news page) and create a nice list using the itemList node. It seems that the current version in n8n (0.141.1) requires extracting each variable one by one. Hopefully, in the future, it will be possible to create the table using just one itemList node. Another nice feature of the workflow is an automatically generated file name with the resulting table. Check out the fileName option of the Spreadsheet File node: ``` Ycombinator_news_{{new Date().toISOString().split('T', 1)[0]}}.{{$parameter['fileFormat']}} ``` The resulting table is saved as an .xls file and delivered via email. ![workflow-screenshot](fileId:543)

Compare Lists and Identify Common Items and Differences Using Custom Keys

This workflow compares two lists of objects (List A and List B) using a user-specified key (e.g., `email`, `id`, `domain`) and returns: - Items common to both lists (based on the key) - Items only in List A - Items only in List B ### How it works: 1. Accepts a JSON input containing: - `listA`: the first list of items - `listB`: the second list of items - `key`: the field name to use for comparison 2. Performs a field-based comparison using the specified key 3. Returns a structured output: - `common`: items with matching keys (only one version retained) - `onlyInA`: items found only in List A - `onlyInB`: items found only in List B ### Example Input: ```json { "key": "email", "listA": [ { "email": "alice@example.com", "name": "Alice" }, { "email": "bob@example.com", "name": "Bob" } ], "listB": [ { "email": "bob@example.com", "name": "Bobby" }, { "email": "carol@example.com", "name": "Carol" } ] } ``` ### Output: - `common`: `[ { "email": "bob@example.com", "name": "Bob" } ]` - `onlyInA`: `[ { "email": "alice@example.com", "name": "Alice" } ]` - `onlyInB`: `[ { "email": "carol@example.com", "name": "Carol" } ]` ### Use Cases: - Deduplicate data between two sources - Find overlapping records - Identify new or missing entries across systems This workflow is useful for internal data auditing, list reconciliation, transaction reconciliation, or pre-processing sync jobs.

Extract, Transform LinkedIn Data with Bright Data MCP Server & Google Gemini

### Disclaimer This template is only available on n8n self-hosted as it's making use of the community node for MCP Client. ![Extract, Transform LinkedIn Data with Bright Data.png](fileId:1221) ### Who this is for? The Extract, Transform LinkedIn Data with Bright Data MCP Server & Google Gemini workflow is an automated solution that scrapes LinkedIn content via Bright Data MCP Server then transforms the response using a Gemini LLM. The final output is sent via webhook notification and also persisted on disk. This workflow is tailored for: 1. **Data Analysts**: Who require structured LinkedIn datasets for analytics and reporting. 2. **Marketing and Sales Teams**: Looking to enrich lead databases, track company updates, and identify market trends. 3. **Recruiters and Talent Acquisition Specialists**: Who want to automate candidate sourcing and company research. 4. **AI Developers**: Integrating real-time professional data into intelligent applications. 5. **Business Intelligence Teams**: Needing current and comprehensive LinkedIn data to drive strategic decisions. ### What problem is this workflow solving? Gathering structured and meaningful information from the web is traditionally slow, manual, and error-prone. This workflow solves: 1. Reliable web scraping using Bright Data MCP Server LinkedIn tools. 2. LinkedIn person and company web scraping with AI Agents setup with the Bright Data MCP Server tools. 3. Data extraction and transformation with Google Gemini LLM. 4. Persists the LinkedIn person and company info to disk. 5. Performs a Webhook notification with the LinkedIn person and company info. ### What this workflow does? This n8n workflow performs the following steps: 1. **Trigger**: Start manually. 2. **Input URL(s)**: Specify the LinkedIn person and company URL. 3. **Web Scraping (Bright Data)**: Use Bright Data's MCP Server, LinkedIn tools for the person and company data extract. 4. **Data Transformation & Aggregation**: Uses the Google LLM for handling the data transformation. 5. **Store / Output**: Save results into disk and also performs a Webhook notification. ### Pre-conditions 1. Knowledge of Model Context Protocol (MCP) is highly essential. Please read this blog post - [model-context-protocol](https://www.anthropic.com/news/model-context-protocol) 2. You need to have the [Bright Data](https://brightdata.com/) account and do the necessary setup as mentioned in the **Setup** section below. 3. You need to have the Google Gemini API Key. Visit [Google AI Studio](https://aistudio.google.com/) 4. You need to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) 5. You need to install the [n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp) ### Setup 1. Please make sure to set up n8n locally with MCP Servers by navigating to [n8n-nodes-mcp](https://github.com/nerding-io/n8n-nodes-mcp) 2. Please make sure to install the Bright Data MCP Server [@brightdata/mcp](https://www.npmjs.com/package/@brightdata/mcp) on your local machine. 3. Sign up at [Bright Data](https://brightdata.com/). 4. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. 5. Create a Web Unlocker proxy zone called mcp_unlocker on Bright Data control panel. 6. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). 7. In n8n, configure the credentials to connect with MCP Client (SDIO) account with the Bright Data MCP Server as shown below. ![MCPClientAccount.png](fileId:1220) Make sure to copy the Bright Data API_TOKEN within the Environments textbox above as API_TOKEN=<your-token>. 8. Update the LinkedIn URL person and company workflow. 9. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. 10. Update the file name and path to persist on disk. ### How to customize this workflow to your needs 1. **Different Inputs**: Instead of static URLs, accept URLs dynamically via webhook or form submissions. 2. **Data Extraction**: Modify the **LinkedIn Data Extractor** node with the suitable prompt to format the data as you wish. 3. **Outputs**: Update the Webhook endpoints to send the response to Slack channels, Airtable, Notion, CRM systems, etc.

BIN Code Lookup with apilayer API

This workflow is designed to validate and fetch information about a card using the BIN code. It utilizes apilayers BIN Check API and provides details like the card brand, type, issuing bank, and country. Prerequisites: - An apilayer account - API Key for the BIN Check API --- Steps in n8n: **Step 1: Manual Trigger** - Node Type: Manual Trigger - Purpose: Starts the workflow manually **Step 2: Set BIN Code and API Key** - Node Type: Set - Fields to set: - bin_code: A sample BIN like JH4KA7560RC003647 - apikey: Your apilayer API key **Step 3: HTTP Request** - Node Type: HTTP Request - Method: GET - URL: https://api.apilayer.com/bincheck/{{ $json.bin_code }} - Headers: - Name: apiKey - Value: {{ $json.apikey }} **(Optional) Step 4: Handle the Output** - Add nodes to store, parse, or visualize the API response. --- Expected Output: The response from apilayer contains detailed information about the provided BIN: - Card scheme (e.g., VISA, MasterCard) - Type (credit, debit, prepaid) - Issuing bank - Country of issuance --- Example Use Case: Use this to build a fraud prevention microservice, pre-validate card data before sending to payment gateways, or enrich card-related logs.

Get Disqus Forum Details Instantly

Simple n8n workflow to fetch detailed information about a Disqus forum (e.g., hackernoon) via manual trigger. Ideal for quick data retrieval in marketing and analytics.

Markdown Timesheet Report Generation

This tutorial demonstrates the creation of the HTML report via Markdown node. The main idea is to prepare a very long text variable via the Function Node and then convert it to the HTML file. The resulting report can be downloaded from the workflow canvas directly or sent via email as an attachment. ![markdownworkflow.png](fileId:647)

Get Workflows Affected by 0.214.3 Migration

If you previously upgraded to n8n version `0.214.3`, some of your workflows might have been accidentally rewired in the wrong way. This issue affected nodes with more than one output, such as `If`, `Switch`, and `Compare Datasets`. This workflow helps you identify potentially affected workflows and nodes that you should check. **Please ensure that you run this workflow as the instance owner.**

Automate Airtable Record Retrieval and HTTP Data Transmission with Delay

Efficiently search Airtable records, introduce a delay, and transmit the data via HTTP request for seamless integration and processing.

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