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Task:
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.
Solution:
We have offered our code base:
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Migrated all ETL pipelines from Python scripts to Airflow, adding all Airflow benefits
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Migrated the existing DWH by changing the Redshift cluster and moving the heaviest data sources to Redshift Spectrum
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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%

Backoffice Application

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