Overview
Finnpick is a Fintech platform that provides curated insights on listed companies, helping investors make informed decisions. The platform relied heavily on accurate, up-to-date market data, including daily stock listings, open/close statuses, and related financial metrics.
But the existing system required manual data entry and updates each day whenever a stock was listed, opened, or closed. This consumed significant team effort and also introduced potential delays and human errors, impacting the freshness and reliability of data displayed to users.
Industry
Fintech / Investment Research
Services
Automated Data Pipeline for Stock Listings and Update
Location
Chennai, India
Technologies
Node.js(Express), PostgreSQL, Cron Jobs (Node-Cron), Third-Party Financial APIs, API Key Rotation, Access-Level Controls, Secure Data Caching, and AWS (Lambda, RDS, CloudWatch)
Challenges Faced By Finnpick
Finnpick’s platform depended on timely and accurate stock market data to deliver reliable insights to investors. But their internal process required daily manual updates each time a company’s stock was listed, opened, or closed. This routine became increasingly time consuming as the platform grew, creating delays and inconsistencies that impacted both the user experience and the platform’s overall credibility.
- Manual and Redundant Data Updates
- Scalability Limitations as Listings Grew
- Delayed Data Visibility for Users
- High Risk of Data Inconsistencies
Our Approach: Building a Scalable Data Engine
We designed a fully automated data ingestion and update system leveraging scheduled background jobs (cron) and API-driven data synchronization.
Data Source Integration
Connected Finnpick’s backend to trusted financial APIs and market feeds, enabling real-time access to stock listing and trading data.
Automated Data Fetching
Implemented cron jobs that periodically fetch the latest data, including stock open/close status, listing changes, and price movements, without any manual intervention.
Data Validation & Normalization
Introduced pre-processing layers that validate incoming data, normalize formats, and eliminate redundancies before updating the live database.
Dynamic Dashboard Update
The web interface and internal admin panel were re-engineered to reflect updates instantly, ensuring users always see the latest verified data.
Failover & Logging
Built-in monitoring and fallback routines to handle API downtime, ensuring system reliability and auditability.
Key Outcomes
100% Elimination of Manual Updates
Real-Time Accuracy
3× Improved Efficiency
Scalable Foundation
Eliminate manual updates with Absolute App Labs!
Scale to Thousands of Listings With Zero Additional Effort.