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Amazon Redshift -
AWS DMS -
HP-UX -
Linux -
Redshift Spectrum -
AWS Backup -
Python
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AIX
The client, a data-focused organization, faced inefficiencies in their ETL process, impacting data integration speed and performance. They required a solution to streamline complex data synchronizations and improve processing efficiency in their data warehouse (DWH).
Python, PostgreSQL, Airflow
PySpark, Apache Spark, PostgreSQL, Airflow
4-6 Weeks
We switched the synchronization of large tables to PySpark, resulting in a 60% reduction in processing time and a more efficient, scalable ETL process. The new system improved the speed of data integration and allowed for more complex data processing without impacting performance.
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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