What’s the difference between HDFS and S3?

The emergence of cloud-based storage solutions like Amazon S3 has prompted many enterprises to transition from traditional on-premises data management systems such as the Hadoop Distributed File System (HDFS) to more scalable and flexible open data lakehouse architectures. HDFS, a core component of the Hadoop framework, was designed to manage large data volumes on commodity hardware within data centers, prioritizing read throughput, fault tolerance, and data locality. In contrast, Amazon S3 offers a scalable, cloud-based object storage system that allows businesses to build data architectures without the expense of maintaining physical infrastructure. Open-source analytics engines like Apache Spark and Trino enhance the capabilities of both HDFS and S3 by enabling large-scale data processing and querying, with Trino providing a federated approach that unifies data access across multiple sources. The shift towards data lakehouses, supported by components such as Trino and open formats like Parquet and Iceberg, allows for dynamic scalability, streamlined data management, and greater accessibility, reducing the reliance on traditional Hadoop ecosystems. Additionally, services like Amazon EMR facilitate the cloud-based deployment of Hadoop, although they introduce complexities related to cost, specialized skills, and multi-cloud flexibility. Companies like Starburst offer advanced solutions for managing data lakehouses, enhancing data accessibility, and ensuring robust security and governance on platforms like Amazon S3.