Integrate MongoDB with 500+ apps and services

Filters articles based on keywords, checks against MongoDB for unique links, then sends results to different webhooks

Companion workflow for MongoDB node docs

This workflow allows you to collect tweets, store them in MongoDB, analyse their sentiment, insert them into a Postgres database, and post positive tweets in a Slack channel. Cron node: Schedule the workflow to run every day Twitter node: Collect tweets MongoDB node: Insert the collected tweets in MongoDB Google Cloud Natural Language node: Analyse the sentiment of the collected tweets Set node: Extract the sentiment score and magnitude Postgres node: Insert the tweets and their sentiment score and magnitude in a Posgres database IF node: Filter tweets with positive and negative sentiment scores Slack node: Post tweets with a positive sentiment score in a Slack channel NoOp node: Ignore tweets with a negative sentiment score

This workflow allows extracting data from multiple pages website. The workflow: 1) Starts in a country list at https://www.theswiftcodes.com/browse-by-country/. 2) Loads every country page (https://www.theswiftcodes.com/albania/) 3) Paginates every page in the country page. 4) Extracts data from the country page. 5) Saves data to MongoDB. 6) Paginates through all pages in all countries. It uses getWorkflowStaticData('global') method to recover the next page (saved from the previous page), and it goes ahead with all the pages. There is a first section where the countries list is recovered and extracted. Later, I try to read if a local cache page is available and I recover the cached page from the disk. Finally, I save data to MongoDB, and we paginate all the pages in the country and for all the countries. I have applied a cache system to save a visited page to n8n local disk. If I relaunch workflow, we check if a cache file exists to discard non-required requests to the webpage. If the data present in the website changes, you can apply a Cron node to check the website once per week. Finally, before inserting data in MongoDB, the best way to avoid duplicates is to check that swift_code (the primary value of the collection) doesn't exist. I recommend using a proxy for all requests to avoid IP blocks. A good solution for proxy plus IP rotation is scrapoxy.io. This workflow is perfect for small data requirements. If you need to scrape dynamic data, you can use a Headless browser or any other service. If you want to scrape huge lists of URIs, I recommend using Scrapy + Scrapoxy.

Overview This automated workflow fetches Upwork job postings using Apify, removes duplicate job listings via MongoDB, and sends new job opportunities to Slack. Key Features: Automated job retrieval** from Upwork via Apify API Duplicate filtering** using MongoDB to store only unique jobs Slack notifications** for new job postings Runs every 20 minutes** during working hours (9 AM - 5 PM) This workflow requires an active Apify subscription to function, as it uses the Apify Upwork API to fetch job listings. Who is This For? This workflow is ideal for: Freelancers looking to track Upwork jobs in real time Recruiters automating job collection for analytics Developers who want to integrate Upwork job data into their applications What Problem Does This Solve? Manually checking Upwork for jobs is time-consuming and inefficient. This workflow: Automates job discovery based on your keywords Filters out duplicate listings, ensuring only new jobs are stored Notifies you on Slack when new jobs appear How the Workflow Works 1. Schedule Trigger (Every 20 Minutes) Triggers the workflow at 20-minute intervals Ensures job searches are only executed during working hours (9 AM - 5 PM) 2. Query Upwork for Jobs Uses Apify API to scrape Upwork job posts for specific keywords (e.g., "n8n", "Python") 3. Find Existing Jobs in MongoDB Searches MongoDB to check if a job (based on title and budget) already exists 4. Filter Out Duplicate Jobs The Merge Node compares Upwork jobs with MongoDB data The IF Node filters out jobs that are already stored in the database 5. Save Only New Jobs in MongoDB The Insert Node adds only new job listings to the MongoDB collection 6. Send a Slack Notification If a new job is found, a Slack message is sent with job details Setup Guide Required API Keys Upwork Scraper (Apify Token) – Get your token from Apify MongoDB Credentials – Set up MongoDB in n8n using your connection string Slack API Token – Connect Slack to n8n and set the channel ID (default: #general) Configuration Steps Modify search keywords in the 'Assign Parameters' node (startUrls) Adjust the Working Hours in the 'If Working Hours' node Set your Slack channel in the Slack node Ensure MongoDB is connected properly Adjust the 'If Working Hours' node to match your timezone and hours, or remove it altogether to receive notifications and updates constantly. How to Customize the Workflow Change keywords: update the startUrls in the 'Assign Parameters' node to track different job categories Change 'If Working Hours': Modify conditions in the IF Node to filter times based on your needs Modify Slack Notifications: Adjust the Slack message format to include additional job details Why Use This Workflow? Automated job tracking without manual searches Prevents duplicate entries in MongoDB Instant Slack notifications for new job opportunities Customizable – adapt the workflow to different job categories Next Steps Run the workflow and test with a small set of keywords Expand job categories for better coverage Enhance notifications by integrating Telegram, Email, or a dashboard This workflow ensures real-time job tracking, prevents duplicates, and keeps you updated effortlessly.