Apache Hadoop vs Apache Spark: What are the Differences?

Apache Hadoop and Apache Spark are both pivotal frameworks in big data processing with distinct histories and functionalities. Hadoop, designed to run on commodity hardware, revolutionized data processing for companies like Yahoo! by providing a cost-effective alternative to expensive proprietary data warehouses. Its framework consists of four core modules, including Hadoop Common, HDFS, Hadoop YARN, and Hadoop MapReduce, which collectively enabled efficient data processing at an internet scale. Conversely, Apache Spark emerged from the University of California, Berkeley, as a faster, in-memory processing engine ideal for machine learning and data science applications. Spark distinguishes itself with features like Spark SQL, a machine learning library, and support for real-time data processing with Structured Streaming. Both technologies have given rise to modern solutions like Trino, which offers a SQL-based interface for querying data across multiple sources without relying on MapReduce, thereby facilitating a smoother transition from traditional Hadoop infrastructures to modern cloud-based data lakehouses. Trino, alongside Apache Iceberg, provides an efficient, low-latency, and flexible platform for handling diverse data types and complex analytics workloads, making it an attractive option for enterprises migrating from Hadoop to scalable cloud environments.