Development of unified synchronization scripts for data engineering

About project:

Client overview

A leading software development company sought to streamline their ETL processes across multiple data sources, aiming to replace their time-intensive manual scripting approach with a more efficient, automated solution. The client’s development team needed a standardized method to handle data integrations with various databases including PostgreSQL, MongoDB, and Elasticsearch.

Tech Stack:

Python, SQL, Apache Airflow, traditional ETL tools

Tech stack after migration:

Python, SQL, Apache Airflow, optimized ETL framework, unified data integration scripts

Time to deliver project:

6-8 weeks

Problem

  • The client needed a faster method for creating ETL processes involving multiple data sources. The existing process was time-consuming and required manually writing scripts for each new integration, slowing down development.

Inspection

  • To address this, we developed a modular repository that can be easily imported into any project. The repository includes pre-built modules responsible for data loading, filtering, transformation, and writing to target tables. Each module communicates with the next using a standardized data format, allowing for easy addition of new data source integrations. We implemented integrations for key databases like PostgreSQL, MongoDB, and Elasticsearch. Now, to set up a new ETL process, developers only need to export the necessary module, initialize the required integration classes, provide access credentials, and specify the desired fields. This approach significantly reduces the time and effort needed to set up new ETL pipelines, streamlining the process into a simple configuration task.

Recommendation

  • It is advisable to implement tools that simplify the setup of new ETL processes, as this can significantly improve developer productivity and speed up project execution.

Resolution

We implemented a system of typical ETL syncs, reducing the setup time for new ETL processes by 80%. This solution enhanced developer productivity and allowed for quicker and more efficient integration of new data sources.

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