Niche 9: Data Management & Reporting
I Built a Real-Time Business Dashboard for Free Using n8n and Google Sheets
We desperately needed a dashboard to track our key business metrics, but tools like Geckoboard were too expensive. I decided to build our own using Google Sheets. The sheet is designed with charts and graphs to be our visual dashboard. The magic is an n8n workflow that runs every 15 minutes. It connects to our Stripe, Google Analytics, and CRM APIs, pulls the latest data, and updates the “data” tab of the Google Sheet. The charts update automatically, giving us a real-time business dashboard for free.
Stop Cleaning Spreadsheets By Hand: The “Data Janitor” Workflow
Every month, I’d get a sales report from a partner that was a formatting nightmare. Dates were inconsistent, names had extra spaces, and country codes were all different. I used to spend an hour cleaning it by hand. I built a “data janitor” workflow in n8n. Now, I just drop the messy spreadsheet into a folder. The workflow reads the file, and then a series of nodes automatically standardizes the date format, trims whitespace from names, and uses a lookup table to convert country codes. The clean file is then saved, ready for analysis.
How We Merged Data from 5 Different Sources into One Master Report
To create our monthly marketing report, our team lead had to pull data from Facebook Ads, Google Ads, Google Analytics, our CRM, and our email platform. It was a manual, error-prone process. I built a master reporting workflow. Every month, n8n connects to all five APIs, pulls the relevant data for the period, and merges it all based on the campaign name. It then populates a single, consolidated Google Sheet, giving us a unified view of our marketing performance without any manual copy-pasting.
This n8n Workflow Syncs Our Production Database to a Reporting Warehouse
Our analysts needed to run complex queries for their reports, but running them on our live production database was risky and could slow down our application. We set up a separate “reporting” database. To keep it updated, an n8n workflow runs every hour. It connects to our production database, queries for any rows that have been created or updated in the last hour, and then upserts (updates or inserts) those changes into the reporting warehouse. Our analysts now have a near real-time, safe environment for their work.
I Built a System to Automatically Generate and Email PDF Reports
We needed to send a weekly performance report to a key stakeholder who wasn’t tech-savvy and just wanted a simple PDF. I built a report generator. An n8n workflow runs every Monday morning. It first pulls the necessary data from our database. Then, it uses a tool like APITemplate.io to populate a pre-designed report template with the data and charts. The service returns a PDF, which the n8n workflow then attaches to an email and sends directly to the stakeholder. The entire process is now automated.
How to Turn Any API into a Live-Updating Google Sheet
I wanted to track the number of stars on our company’s GitHub repository in a Google Sheet. I built a simple workflow that does just that. An n8n workflow runs once an hour. It uses the HTTP Request node to call the GitHub API and get the current star count for our repo. It then uses the Google Sheets node to append a new row with the current timestamp and the star count. The result is a time-series log of our repository’s growth, and this same principle can be applied to any API.
This Workflow “Enriches” My Data by Calling 3 Different APIs for Each Row
I had a spreadsheet of company names, but I needed more information for my sales outreach. I built a data enrichment workflow. The workflow reads each company name from the spreadsheet. For each one, it first calls the Clearbit API to get the company’s domain and employee count. Then, it calls a tech-stack-finder API to see what software they use. Finally, it calls the Google News API to find any recent news about them. It then writes all this new, “enriched” data back into the spreadsheet.
I Replaced Google Data Studio Connectors with n8n for More Flexibility
The native connectors in Google Data Studio were often limited or didn’t exist for the specific data sources I needed. I started using n8n as a more powerful, universal connector. For example, to get data from a niche CRM, I have an n8n workflow that calls that CRM’s API, cleans and transforms the data exactly how I need it, and then writes the result to a Google Sheet. I then connect Data Studio to that Google Sheet. n8n gives me complete control over the data before it ever reaches the dashboard.
How to Build a Data Pipeline That’s Cheaper Than Fivetran/Stitch
We needed to get our data from our production Postgres database into a Snowflake data warehouse, but tools like Fivetran were quoted at over 1,000 dollars a month. I built our own data pipeline with n8n. An n8n workflow runs every 30 minutes. It connects to our Postgres DB, queries for any new or updated records since the last run, and then connects to Snowflake and loads the data. It’s a simple but robust ETL (Extract, Transform, Load) process that costs us pennies to run, saving us a fortune.
This Workflow Validates and Cleans Data from User-Submitted Forms
The data coming from our website’s contact form was a mess. People would enter “california” or “CA” for the state, and phone numbers were in all different formats. I built a validation and cleaning workflow. Now, when the form is submitted, it first goes to an n8n webhook. The workflow standardizes the state to the two-letter abbreviation, removes all non-numeric characters from the phone number, and properly capitalizes the name before passing the clean data to our CRM. It has drastically improved our data quality.
I Built a “Looker Clone” for My Startup with n8n and Metabase
We wanted the power of a BI tool like Looker for data exploration, but couldn’t afford the price tag. I built an open-source alternative. We use Metabase as our front-end for creating dashboards and charts. The magic is that n8n handles all the complex data preparation. We have n8n workflows that run in the background, pulling data from all our different SaaS tools, cleaning it, and loading it into a central database that Metabase is connected to. We get 80% of the power of Looker for 1% of the cost.
How to Archive Your Data from Airtable to a SQL Database Automatically
Our main Airtable base was approaching the 50,000-record limit and was getting slow. I built an archiving workflow. Every month, the n8n workflow scans the Airtable base for any records that are marked as “Closed” and are older than 90 days. It then copies these records into a more permanent, long-term storage PostgreSQL database. After successfully copying them, it deletes the records from Airtable. This keeps our active Airtable base fast and lean without losing any historical data.
This n8n Workflow Creates Pivot Tables and Charts on Autopilot
My manager always asked for a pivot table showing sales by region, and I had to create it manually in Excel every week. I automated it. My n8n workflow first fetches the raw sales data from our database. It then uses a special node to perform the pivot table operation directly within the workflow, summarizing the total sales for each region. It then uses a charting library to generate a bar chart image of the results and posts both the table and the chart to Slack.
I Built a “Data Dictionary” for Our Company by Scraping Our Database Schema
New employees were always confused about what the different tables and columns in our database meant. Our documentation was nonexistent. I built a workflow to create a data dictionary. The workflow connects to our database and runs a query to get the schema information (all table names, column names, and data types). It then populates this information into a clean, searchable Airtable base. We can then manually add plain-English descriptions for each field. The result is a living, breathing documentation for our data.
How to Sync Data Between a SQL and NoSQL Database
Our main application runs on a MongoDB (NoSQL) database, but our finance team needed the data in a SQL format for their reporting tools. I built a sync workflow. Every hour, the n8n workflow queries our MongoDB database for any new customer or order records. It then transforms the flexible JSON structure from Mongo into the rigid, table-based structure required by our PostgreSQL reporting database and inserts the new records. This allows both teams to work with the data in the format they’re most comfortable with.
This Workflow Transforms Complex JSON into a Simple CSV File
I needed to get data from a third-party API, but it returned a deeply nested, complicated JSON structure that was impossible to work with in a spreadsheet. I built a transformation workflow. I can make the API call with n8n’s HTTP Request node. Then, I use the “Set” and “Merge” nodes to carefully navigate the JSON, pull out only the specific fields I need (like data.items[0].user.firstName), and restructure them into a simple, flat format. The final step saves this clean data as a simple CSV file.
I Built a System to Anonymize and Sanitize Data for Development Environments
Our developers needed a copy of the production database to test on, but using real customer data was a major privacy and security risk. I built a data sanitization pipeline. The n8n workflow takes a fresh backup of our production database. It then iterates through the user table, replacing real names with fake ones, scrambling email addresses, and changing all passwords. This anonymized version of the database is then restored to the development environment, giving our developers realistic but safe data to work with.
How to Create Custom Alerts Based on Your Business KPIs
We wanted to be notified if our key business metrics dropped below a certain threshold. I built a KPI alerting system. An n8n workflow runs every hour. It queries our database for the number of new signups in the last hour. If that number is below a predefined threshold (e.g., less than 10), it sends a high-priority alert to our #growth channel in Slack. This allows us to react quickly to any potential issues, like a broken signup form or a failing ad campaign.
This Workflow Fetches Financial Market Data and Creates a Daily Briefing
I’m an avid investor and wanted a personal, daily briefing on the markets without having to check multiple websites. I built a workflow to create one for me. Every morning, the n8n workflow connects to a financial data API. It pulls the previous day’s closing price for the stocks in my portfolio, the current price of major cryptocurrencies, and the top headlines from financial news sites. It then formats all this into a single, clean email that’s waiting in my inbox when I wake up.
I Built a “Data Quality” Score for Our CRM Records
Our CRM was full of incomplete and inconsistent records, which made it unreliable for our sales team. I built a “data quality” scoring system. Every night, an n8n workflow scans all our contact records. It checks for common issues: is the “email” field valid? Is the “phone number” field filled out? Is the “company name” field not “N/A”? It then calculates a “Data Quality Score” for each record and flags any below 70% for manual review and cleanup.
How to Automate ETL/ELT Processes Without Writing a Single Line of Python
I needed to move data from our Intercom account into our BigQuery data warehouse, a classic ETL (Extract, Transform, Load) task. Instead of writing a complex Python script, I built it visually in n8n. The workflow uses the Intercom node to Extract the data. It then uses a series of standard n8n nodes to Transform the data (e.g., clean up fields, format dates). Finally, it uses the BigQuery node to Load the data into our warehouse. It’s a robust data pipeline with zero custom code.
This n8n Workflow Back-fills Missing Data in Our Analytics
We discovered that our analytics tracking was broken for a week, leaving a huge gap in our data. Manually fixing it would have been impossible. I built a back-filling workflow. The workflow read the raw server logs from that week (which were still intact). It then parsed these logs to identify the user actions that our analytics had missed. Finally, it used the analytics platform’s API to send back-dated events, effectively “replaying” the missing week and filling the gap in our charts.
I Built a Simple “Change Data Capture” (CDC) System with n8n
We needed a way to know exactly when a record in a specific database table changed. I built a simple Change Data Capture (CDC) system. The n8n workflow runs every minute. It queries the target table for any rows where the updated_at timestamp is in the last minute. If it finds any changed rows, it sends a message with the row’s ID and the changed data to a webhook. This allows other systems to react to database changes in near real-time without constantly polling the database themselves.
How to Connect n8n to BigQuery for Large-Scale Data Processing
We were collecting millions of events a day, and our traditional PostgreSQL database couldn’t handle the analytical queries. We moved to Google BigQuery. This guide shows how we used n8n to manage this. We set up an n8n workflow that receives data from our application via a webhook. Instead of writing to Postgres, it now batches the data and uses the BigQuery node to stream it directly into our data warehouse. It also shows how to use n8n to run and schedule queries against BigQuery.
This Workflow Migrated 1 Million Records from an Old System with Zero Downtime
We had to migrate a million customer records from our old, legacy CRM to our new one. A simple export/import was not an option due to the risk of downtime and data loss. I built a careful, phased migration workflow in n8n. The workflow read records from the old system in small batches, transformed the data to fit the new system’s schema, and loaded them via the new CRM’s API. It kept a detailed log and could be paused and resumed. This allowed us to perform a massive migration over a weekend with zero disruption.
I Built a “Master Customer Record” by Combining Stripe, HubSpot, and Intercom Data
Our customer data was fragmented. A user’s payment history was in Stripe, their marketing interactions were in HubSpot, and their support conversations were in Intercom. I built a workflow to create a single master record. The workflow takes a user’s email, then calls the APIs for all three services to pull their data. It then merges this information into one comprehensive JSON object and saves it in a central “customer 360” database. Our teams now have a complete view of every customer relationship.
How to Create “Snapshot” Reports of Your Data at Regular Intervals
I needed to track our key metrics at the end of every single month for historical reporting. I built a “snapshot” workflow. On the last day of every month at 11:59 PM, the n8n workflow runs. It queries our database for all our main KPIs (e.g., total users, monthly recurring revenue). It then writes these values, along with the date, into a permanent “Monthly Snapshots” table. This gives us a clean, historical record of our growth that is never affected by later data corrections or changes.
This Workflow Automates the Entire Process of Gathering Data for a Board Meeting
Preparing our quarterly board deck used to take a week of frantic data gathering from different departments. I automated it. Two weeks before the board meeting, an n8n workflow kicks off. It sends automated Slack reminders to the heads of Sales, Marketing, and Product to update their numbers in a shared Google Sheet. A week later, the workflow pulls that data, along with data from our production database, and populates a pre-designed Google Slides template for the board deck.
I Built a Tool to “Reverse ETL” Data from Our Warehouse Back into Our SaaS Apps
Our data warehouse had a calculated “customer health score” for each user, but our sales team couldn’t see it because they live in our CRM. I built a “Reverse ETL” pipeline. An n8n workflow runs daily. It queries our data warehouse for the latest health scores for all customers. It then iterates through the list and uses our CRM’s API to update a custom “Health Score” field on each contact record. This pushes valuable insights from our data warehouse back into the operational tools our teams use every day.
How to Monitor Your Data Pipelines for Failures and Delays
Our data pipelines were “black boxes.” If they failed, we often didn’t know until someone complained about a stale report. I built a monitoring system. I wrapped our main data pipeline workflow inside another n8n workflow. This “meta” workflow triggers the data pipeline. If the pipeline workflow fails, the parent workflow catches the error and sends a detailed alert to our #data-alerts Slack channel. It also has a timeout: if the pipeline takes too long to run, it sends a “delay” alert.
This Workflow Generates Sample Data for Testing and Demos
For sales demos, we wanted to show our product with a rich, realistic-looking dataset, but we couldn’t use real customer data. I built a sample data generator. The workflow uses AI and data generation libraries to create hundreds of fake but plausible user profiles, projects, and activity logs. It then populates a fresh database with this data. We can now spin up a brand new, fully-populated demo environment in minutes, giving every potential customer a compelling and safe product tour.
I Built a “Google Analytics” Alternative with n8n and a Simple Database
I was becoming increasingly concerned about the privacy implications of using Google Analytics on my website. I decided to build my own simple, privacy-focused analytics system. I created a small JavaScript snippet that sends a “pageview” event to an n8n webhook. The workflow records the page path, referrer, and an anonymized user ID in a simple database. Another n8n workflow powers a basic dashboard to show me my top pages and traffic sources. It gives me the core insights I need without compromising user privacy.
How to Create a Time-Series Analysis of Any Metric You Can Track
I wanted to see how our number of active users was trending over time. I built a simple time-series logging workflow. Every single day, an n8n workflow runs a query against our database to count the number of users who were active in the last 24 hours. It then appends this number, along with today’s date, to a dedicated Google Sheet. By connecting this sheet to a charting tool, I can now easily visualize our daily active user trend and spot patterns over weeks and months.
This Workflow Compares Our Performance Data Against Industry Benchmarks
We knew our email open rate was 25%, but we had no idea if that was good or bad. I built a benchmarking workflow. The workflow fetches our key performance metrics (like email open rates, ad click-through rates, and conversion rates). It then scrapes data from trusted industry reports and marketing blogs to find the average benchmarks for our sector. It then presents a report comparing our numbers to the industry average, showing us where we’re excelling and where we need to improve.
I Use n8n to Run SQL Queries Against Our Database from a Slack Command
Our non-technical team members often had simple data questions, but they had to wait for an engineer to help them. I empowered them to get the data themselves. I created a Slack command, /ask-data, that is connected to an n8n workflow. A team member can write a pre-approved, simple question. The n8n workflow matches their question to a pre-written, safe SQL query, runs it against our reporting database, and posts the results back in a clean, formatted table in Slack.
How to Automate Data Entry from PDFs and Scanned Documents
We receive hundreds of invoices from our vendors as PDF attachments in emails. Manually entering the details into our accounting software was a full-time job. I built a workflow to automate it. When an invoice email arrives, the workflow sends the PDF to an intelligent document processing API. The AI extracts the key fields—invoice number, due date, amount, and line items. The workflow then uses this structured data to create a new bill in our accounting software automatically.
This n8n Workflow Creates a “Single Source of Truth” for Our Product Catalog
Our product information was scattered across our e-commerce store, our ERP, and various spreadsheets. It was a mess. I used n8n to create a “single source of truth.” We designated an Airtable base as our master product catalog. Now, if a product manager updates a price in Airtable, an n8n workflow automatically pushes that change to our Shopify store and our internal ERP. It ensures that our product data is consistent across all our systems, all the time.
I Built a “Data Storytelling” Report That Highlights Key Trends
Just sending a dashboard full of charts wasn’t enough; my team needed to know what the data meant. I built a “data storytelling” workflow. The workflow gathers our weekly metrics. It then sends the data to an AI model with a prompt: “You are a data analyst telling a story. Based on this data, what is the most interesting thing that happened this week? What trend is emerging? Write a short narrative explaining the data.” This AI-generated story is now the first thing my team reads in our weekly report.
How to Sync Your Application Data with Elasticsearch for Better Search
Our application’s built-in search was slow and basic. We wanted to implement a powerful search experience using Elasticsearch. I built a workflow to keep the search index perfectly in sync. Now, whenever a record is created or updated in our main application database, a webhook fires to n8n. The n8n workflow then takes that new data, formats it correctly, and indexes it in our Elasticsearch cluster. This ensures that our search results are always as up-to-date as our database.
This Workflow Finds and Merges Duplicate Records in Any Database
Our CRM was full of duplicate contacts—the same person entered multiple times with slight variations in their name or email. I built a de-duplication workflow. The workflow first pulls all contacts from the CRM. It then uses a series of fuzzy matching algorithms to find potential duplicates (e.g., “Jon Smith” and “Jonathan Smith” at the same company). It presents these potential duplicates in a simple interface for a human to review. With one click, I can trigger another workflow to merge the records.
I Built a Predictive Model with n8n That Forecasts Next Month’s Sales
Our sales forecasting was basically just guesswork. I built a simple predictive model with n8n. The workflow pulls our historical sales data from the last two years. It then feeds this time-series data into a simple forecasting model (like a moving average or an exponential smoothing model) directly within the workflow. The output is a prediction for next month’s sales, complete with an upper and lower confidence bound. While not perfect, it gives us a much more data-driven starting point for our financial planning.
How to Automate Your Cohort Analysis Reporting
We wanted to understand customer retention, but creating cohort analysis reports in Excel was incredibly complex and time-consuming. I automated it. An n8n workflow runs once a month. It queries our database for all users and their signup dates, as well as their activity for each subsequent month. It then processes this data to create a classic cohort retention grid (e.g., of users who signed up in January, what percentage were still active in February, March, etc.). The final grid is then written to a Google Sheet.
This Workflow Creates a “Data Health” Dashboard for the Whole Company
Different teams cared about different aspects of our data quality. I built a centralized “Data Health” dashboard to give everyone visibility. The workflow runs daily and performs a series of checks. It checks our CRM for duplicate contacts, our e-commerce platform for products with missing images, and our database for orphaned records. It then updates a dashboard with a “health score” for each area. If any score drops below 90%, it automatically alerts the team responsible.
I Replaced a Costly Business Intelligence (BI) Tool with n8n
We were paying a fortune for a big-name BI tool, but we were only using it to schedule a few key reports. I realized I could replace it with n8n. I rebuilt our top 5 most critical reports as individual n8n workflows. Each workflow queries the necessary data, transforms it as needed, and then emails the results as a CSV or a PDF to the relevant stakeholders on a schedule. By decommissioning the expensive BI tool, we’re now saving over 10,000 dollars a year with no loss of functionality.
How to Build a “Self-Service” Reporting Tool for Non-Technical Teammates
Our marketing team constantly asked me to pull simple data for them. I built a self-service tool to empower them. I created a simple web form with a dropdown menu of pre-defined questions they can ask (e.g., “Show me top 10 landing pages for last week”). When they submit the form, it triggers an n8n workflow. The workflow runs the corresponding pre-written, safe SQL query against our database and returns the results in a clean table right on the web page. They get their data instantly, and I get my time back.
This Workflow Turns Survey Responses into an Insightful Report Instantly
After we ran a customer survey, we’d be left with a giant spreadsheet of raw responses that was hard to interpret. I built a workflow to analyze it automatically. When our survey closes, I can feed the spreadsheet of responses to an n8n workflow. The workflow iterates through the responses, uses AI to analyze the sentiment of open-ended questions, and tallies the quantitative data. It then generates a Google Slides presentation with charts and key takeaways, turning raw data into an insightful report in minutes.
I Built a System to A/B Test Our App’s Features and Report on the Results
We wanted to A/B test a new feature in our app, but we didn’t have a built-in framework for it. I built one with n8n. Our app randomly assigns users to “Group A” or “Group B.” It then sends events to an n8n webhook for key user actions. The workflow logs these events in a database, tagged with the user’s group. After a week, another workflow queries this data, calculates the conversion rates for both groups, determines the statistical significance, and posts the results of the test to Slack.
How to Connect n8n to Power BI for Dynamic Dashboards
My company loves using Microsoft Power BI for its dashboards, but getting data from our non-Microsoft data sources into it was a challenge. I use n8n as the universal connector. I have an n8n workflow that runs on a schedule. It pulls data from our PostgreSQL database and our various SaaS tool APIs. It then cleans and combines this data, and pushes the final, prepared dataset into a location that Power BI can easily connect to, like an Azure SQL database or even a SharePoint list, ensuring our dashboards always have fresh, comprehensive data.
This Workflow Creates a “Customer Journey” Map from Disparate Data Points
I wanted to understand the typical path a customer takes from their first website visit to becoming a loyal user. I built a workflow to map it out. The workflow pulls data from multiple sources: our analytics for their first touchpoint, our CRM for their sales interactions, and our product database for their feature usage. It then stitches these disparate events together into a chronological timeline for each customer. By analyzing hundreds of these timelines, we can see the most common and successful customer journeys.
Your First Data Engineering Project: A Guided n8n Tutorial
The world of data engineering can seem intimidating, with its talk of pipelines, warehouses, and ETL. This guide provides a gentle introduction by walking you through your very first data engineering project using n8n. We’ll build a simple but complete data pipeline: extracting data from a public API (like a weather API), doing a simple transformation (like converting temperatures from Kelvin to Celsius), and loading it into a Google Sheet. It’s a hands-on project that teaches the fundamental concepts in a simple, visual way.