Leading MarTech Company Achieves Real-Time Data Confidence and Seamless Scaling Across 10K+ Locations with DataOps Backbone
Case Study
Leading MarTech Company Achieves Real-Time Data Confidence and Seamless Scaling Across 10K+ Locations with DataOps Backbone
About the Client
Our client is a fast-growing marketing technology scale-up that was on an aggressive expansion spree after proving its product-market fit. Their flagship offering, a marketing intelligence & automation software, had gained traction among multi-location businesses that required promotions tailored to national and regional preferences. The client operated on a software-plus-support model through which they helped businesses create, track, and optimize localized marketing campaigns across search, social media, and third-party websites.
Business Challenge
The client’s marketing platform relied on multiple APIs to fetch live data from diverse web sources such as Google ads, Facebook, and Instagram. But the reliability of these APIs were put to test when the platform expanded to new customers. The company was transitioning from its initial growth phase to industrial growth, an ideal time to modernize its platform for high-velocity digital environments.
As the user count exploded and concurrent marketing campaigns multiplied, two critical DataOps areas required attention: Data Ingestion and Data Observability. In essence, the scale-up sought to ensure the following
Data flows correctly into the platform
Data is displayed correctly on the platform
Trigent Solution
Trigent proposed two fixes to resolve the challenges. First, a data ingestion monitoring layer that would automatically check for API health, completeness, accuracy, and timeliness of the data.
Second, a data observability layer that would provide end-to-end visibility of the data system behavior across ingestion, transformation, and serving – so issues can be detected proactively and solved before they impact customer experience.
Implementation
Data Ingestion Monitoring
- Built postman scripts to continuously check API health, response validity, and payload completeness.
- Monitored record count (completeness) and freshness (timeliness).
- Automated these checks to run on schedule using the Azure DevOps CI/CD pipelines.
- Built test API repositories that made parallel calls, and measured response time, error rates and stability.
- Secured the network through HTTPS/TLS transport; and secured the application through Vulnerability Assessment and Penetration Testing (VAPT) and Data Application Security Testing (DAST) compliant on OWASP.
Data Observability
- Wrote 200 test cases and automated 90 of them in Cyprus, which effectively simulated user actions (filtering, reports download, export) to verify the correctness of the data in the UI. The custom framework automatically performed data quality checks post transformation and during visualization.
- We ran the tests for varying user loads to observe API latency, system responsiveness and concurrency performance.
- Collected logs, metrics, and reports to track ingestion success, API uptime, and system responsiveness.
- Standardized and templated all the above processes into a DevOps center of excellence creating reusable patterns for continuous observability.
Client Benefits
The data ingestion monitoring and data observability layers created a robust DataOps backbone that ensured real-time data confidence for the platform and its users. The solution enhanced the platforms’ reliability, enabling the client to scale seamlessly to thousands of additional users.
- 50+ marketing systems streamlined: Data now flows accurately and on time through the ingestion monitoring layer.
- 200+ Potential Errors Prevented: The data observability layer intercepted API and data display issues before they could impact users.
- 10,000+ locations supported: Marketing campaigns now run seamlessly across 10K+ business locations worldwide.