Implementing PySpark-Driven ETL for High-Performance Data Synchronization

About project:

Client overview

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).

Tech Stack:

Python, PostgreSQL, Airflow

Tech stack after migration:

PySpark, Apache Spark, PostgreSQL, Airflow

Time to deliver project:

4-6 Weeks

Problem

  • The client was facing slow performance and inefficiencies in their ETL process when extracting, transforming, and loading data from multiple sources into the target tables in their data warehouse (DWH).

Inspection

  • To address this, we decided to shift the ETL process for complex data syncs to PySpark. PySpark’s "lazy" evaluation allowed us to efficiently extract data in layers: starting with one source, using its output to feed the next, and so on. Once all the instructions were formed, PySpark processed them quickly and outputted the results into separate delta files. We then created scripts to update the main tables in the DWH using data from these deltas. This separation of data extraction (handled by PySpark) and table updates (managed by the database) significantly improved the speed and reliability of the ETL process.

Recommendation

  • For ETL processes involving multiple data sources and large volumes of data, consider using specialized tools like PySpark. PySpark is scalable and can efficiently allocate resources, making it an ideal choice for handling big data operations.

Resolution

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.

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