⚙️ Is Apache Spark Being Overtaken by ClickHouse, Snowflake, and StarRocks?
Over the last few years, Apache Spark has been the de facto choice for large-scale ETL, batch processing, and ML workloads across data platforms. However, the emergence of modern analytical engines — ClickHouse , Snowflake , and StarRocks — is redefining how teams think about data pipelines and performance optimization. Let’s break down what’s really happening 👇 🧩 Spark: Still the Heavy-Lift Engine Spark remains unmatched for: Complex multi-source ETL and data lake transformations Large-scale joins and machine learning workloads Distributed compute flexibility ( Scala , PySpark , SQL, MLlib , Delta , etc.) But Spark’s batch-oriented execution model introduces startup overhead and cost inefficiencies when used for near real-time analytics or small-scale transformations. ⚡ ClickHouse & StarRocks: Redefining Real-Time OLAP Both ClickHouse and StarRocks are built for low-latency analytical workloads . ClickHouse leverages a columnar MergeTree engin...