The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Automated ETL Pipeline: From Raw Data to a Refreshed Dashboard
Problem: Data came from multiple sources and was cleaned by hand every day. It was slow, error-prone, and reports were often late.
Approach: A Python ETL pipeline extracts data from APIs, CSVs and databases, then cleans and validates it with pandas. It loads the result into a SQL warehouse and triggers a Power BI refresh. A scheduler (Airflow/cron) runs it daily with retries and failure alerts.
Result: Fully automated daily reporting with built-in data quality checks that catch bad records before they reach the dashboard.
Note: Demo project built on sample data.
Tools: Python, pandas, SQL, Airflow, Power BI
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Challenges
View all
Related posts
technical dashboard
AI Lease & Contract Intelligence for Property Managers is a local web application that converts rental agreements into structured, searchable data. Users upload PDF or TXT leases, and the system extracts tenant, property, rent, security deposit, lease dates, notice periods and renewal terms, clearly marking any missing or uncertain details rather than inventing values.
The platform includes a RAG-based contract assistant that answers questions with cited source passages, using a local Ollama model when available and keyword retrieval as a fallback. Automated review flags surface unclear clauses and missing information for human verification, while an expiry tracker and live dashboard help managers stay ahead of renewals and expirations.
Built with Python, Flask and SQLite, it runs entirely on the user’s machine with no paid APIs or subscriptions, keeping sensitive documents private.
I noticed the system flags missing lease fields instead of guessing values, which should cut down on manual verification headaches.
Automated Review Requests for Local Service Businesses
The Problem
Google reviews help local businesses get found, and every HVAC company, salon, cleaner and contractor knows it. Most of them ask for reviews inconsistently. The reason isn’t that they don’t care. Asking every customer, right after every job, depends on someone remembering at the exact moment the business is busiest. Happy customers never get asked, and the review count grows slower than it should.
The Solution
I built an n8n system that sends the review request automatically, once, at the right time, and tracks what happens next. Nobody gets asked twice and nobody gets forgotten.
How the System Works
1. Checks every 15 minutes. The workflow reads a Google Sheet that lists each job and what has already been sent. The sheet is the system’s memory, so nothing depends on timers that can fire twice.
2. Sends the request. Two hours after a job is marked complete, the customer gets an email with a review link.
3. Tracks the click. The link goes through a tracking step that records “clicked” in the sheet, then sends the customer to Google.
4. Sends one follow-up. If there’s no click after three days, one reminder goes out. Customers who clicked or opted out are skipped.
5. Alerts me on errors. A separate error workflow emails me if any step fails.
Why this design: every step re-checks the sheet before sending, so the system can’t double-send and a customer who already responded is never contacted again.
Tools: n8n, Gmail, Google Sheets
I'd test the tracking link with email security scanners before using clicks to suppress reminders. How do you distinguish an automated link check from a customer opening the review page?