🚀 Is Apache Spark Really Dying? Let’s Talk

Photo by Silvestri Matteo on Unsplash

The world of data engineering moves fast. Every few months, a new tool emerges, claiming to be the next big thing.

Lately, I’ve seen a surge of posts predicting the death of Apache Spark. Some engineers argue that Spark is outdated, inefficient, and no longer the best tool for big data processing.

So, let’s take a realistic look at Spark — its strengths, weaknesses, and whether it’s still relevant in 2025 and beyond.

⚡ The Case Against Spark: Why Are Some Engineers Moving On?

To be fair, Spark has limitations, and its shortcomings are exactly why some engineers are looking elsewhere:

❌ Performance Bottlenecks

• JVM Overhead: Spark runs on the JVM, which comes with memory and GC (Garbage Collection) overhead. This makes Spark less efficient for certain workloads compared to native C++-based engines.
• Single-threaded Drivers: The Spark driver can sometimes become a bottleneck, especially when handling large-scale metadata or driver-heavy operations.

❌ Not Ideal for Certain Workloads

• Machine Learning & AI: While Spark MLlib exists, it’s not as efficient as frameworks like TensorFlow, PyTorch, or…