Implemented pandas-based cleaning rules in data_preprocessing.py, transformations for salesorder.csv → clean_salesorder.csv, pipeline testing via multiple DAG runs.

Abstract: This study aims to increase ETL process efficiency »ud reduce processing time by applying the method of Change Data Capture (CDC) in distributed system using Hadoop Distributed file System ...

With the open-source Dataverse SDK for Python (announced in Public Preview at Microsoft Ignite 2025), you can fully harness the power of Dataverse business data. This toolkit enables advanced ...

By combining Databricks, Python, and PySpark, organizations can elevate their ETL testing from a manual, error-prone task to a scalable, automated process. At SDET Tech, we help teams implement ...

End‑to‑End DWBI Project Overview Built a scalable, maintainable pipeline—from data modeling through synthetic data generation and automated Snowflake ingestion to interactive Power BI dashboards—using ...

It's spring training time. Major League Baseball's 30 teams are in Arizona and Florida for the next couple of months, preparing for the upcoming season. And this year, for the first time, big league ...

Technology, changing at a breakneck speed, has never raised higher demands for practitioners who can guarantee the integrity, security, and performance of large-scale applications. Viharika is at the ...

One of the major trends in ETL testing is the adoption of cloud-based platforms and tools, such as AWS Glue, Azure Data Factory, and Google Cloud Dataflow. These services offer scalable, flexible, and ...

Databricks, AWS and Google Cloud are among the top ETL tools for seamless data integration, featuring AI, real-time processing and visual mapping to enhance business intelligence. Extract, transform ...

Earlier this year, I had the privilege of serving on the organizing committee for the DataTune conference in my hometown of Nashville, Tenn. Unlike many database-specific or platform-specific ...