What is Hadoop, and how does it relate to big data?

Hadoop is an open-source framework designed to store and process large datasets across clusters of computers. At its core, Hadoop provides two key components: the Hadoop Distributed File System (HDFS) for storage and the MapReduce programming model for processing. Developed by Apache, it addresses the challenge of handling data that exceeds the capacity of a single machine by distributing tasks across multiple nodes. For example, instead of trying to process a 100 TB dataset on one server, Hadoop splits the data into smaller blocks, distributes them across a cluster, and processes them in parallel. This approach makes it possible to work with data at scale efficiently.

Hadoop’s architecture directly addresses the “three Vs” of big data: volume, velocity, and variety. HDFS stores massive amounts of structured and unstructured data across inexpensive hardware, while MapReduce enables batch processing of this data. For instance, a developer could use MapReduce to analyze log files from thousands of servers: the “map” phase might extract error codes, and the “reduce” phase could tally occurrences. Additionally, Hadoop’s ecosystem includes tools like YARN (Yet Another Resource Negotiator) for resource management and libraries like Apache Hive for querying data with SQL-like syntax. These components make Hadoop a flexible foundation for big data workflows, especially when traditional databases struggle with scale or cost.

While Hadoop is not the only solution for big data, it remains relevant due to its scalability and fault tolerance. Companies like Facebook and Netflix have historically used Hadoop for tasks like recommendation engines and user behavior analysis. However, Hadoop’s batch-oriented processing can be slower for real-time use cases compared to tools like Apache Spark. Developers often integrate Hadoop with other technologies—for example, using Spark for real-time analytics while relying on HDFS for storage. Despite newer alternatives, Hadoop’s ability to handle distributed storage and processing at low cost (using commodity hardware) ensures its continued use in enterprises managing large-scale data pipelines.