top of page

Database Reconstruction


Technologies used:
SQL, Amazon Web Services (AWS), Amazon Redshift, AWS Batch, Docker, Python, Apache Airflow



The customer's data storage had a chaotic structure, the data extraction process became very long, and daily data could not be downloaded in a day. Frequent errors in the database interfered with the work.


We have offered our code base:

  • Migrated all ETL pipelines from Python scripts to Airflow, adding all Airflow benefits

  • Migrated the existing DWH by changing the Redshift cluster and moving the heaviest data sources to Redshift Spectrum

  • Implemented data pipelining to calculate ML model using AWS Batch and Docker


Project Results

Saved storage costs by 50% and increased query performance by 6 times. ETL processes were implemented, which reduced the raw data collection time by 12 times. Resource savings of about 95%

Встреча команды

Backoffice Application

bottom of page