- Amazon Redshift
- AWS DMS
- HP-UX
- Linux
- Redshift Spectrum
- AWS Backup
- Python
- AIX
Enterprise Database Migration and Optimization to Amazon Redshift with Disaster Recovery
BI & Data Engineering
A global financial services provider sought to validate and ensure data completeness across their data warehouse models, requiring a robust solution to verify ETL pipeline accuracy and maintain data integrity throughout their business intelligence infrastructure.
Manual data validation, SQL queries, basic ETL tools
Automated cross-check system, Python, Apache Airflow, SQL, Alerts in Slack
4-6 Weeks
We implemented an automated cross-check system that compares the data between the source-like tables and the models. This system regularly validates data consistency, boosting confidence in the data’s completeness without overloading the database. It has also allowed for more extensive checks during periods of low database activity, ensuring ongoing data accuracy.
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
BI & Data Engineering
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