Data Engineering

In the age of big data, open-source tools have become the backbone of modern data engineering. They help build robust, scalable, and flexible data platforms—without the vendor lock-in or high costs of proprietary solutions. In this blog post, I’ll walk you through 10 powerful open-source tools that every data engineer should know and ideally have in their stack. 1. Apache Airflow 🛠️ Category: Workflow Orchestration Apache Airflow is the industry standard for orchestrating complex data workflows using DAGs (Directed Acyclic Graphs). It lets you schedule and monitor data pipelines, and has a vibrant ecosystem with providers for AWS, GCP, Spark, and more. ...

“The big-data world rewards the right abstraction at the right scale.” After more than a decade of wrestling with datasets that range from a few megabytes to multiple terabytes, I’m often asked “Should I use Pandas or PySpark?” Both libraries live in the Python ecosystem, both expose a DataFrame API, and—on the surface—they feel remarkably similar. Yet they’re built for very different contexts. This post breaks down the key differences so you can pick the right hammer for the job. ...