Apache Spark Evolution: From Hadoop Origins to Spark 4.0 🚀

Curious about the history of 𝐀𝐩𝐚𝐜𝐡𝐞 𝐒𝐩𝐚𝐫𝐤? Did you know Apache Spark was born out of frustration with Hadoop MapReduce back in 2009? Since then, it’s evolved into a powerhouse. From caching in-memory RDDs to structured streaming, adaptive query execution, and now declarative pipelines in Spark 4.0. And this is where DataFlint comes in: helping you optimize, debug, and control your Spark pipelines so you can make the most of every version. Watch the journey in under a minute 👇#apachespark #bigdata #dataengineering #sparkoptimization #DataFlint#AIcopilot