Apache Hadoop HDFS vs Unilake

Apache Hadoop HDFS is a free, open-source distributed file system for high-throughput access to large application data sets on commodity hardware. It is designed for clusters of hundreds of nodes and can support tens of millions of files in one instance. HDFS copies file blocks across machines and detects failed DataNodes so it can start creating replacement copies. Its design prioritizes batch processing and streaming throughput rather than low-latency interactive workloads. Files follow a write-once, read-many model: appends and truncates are supported, but arbitrary in-place changes are not. Applications can connect through Java, a C wrapper, REST, a browser interface or an NFS gateway. The documentation lists connectors for Google Cloud Storage, Amazon S3, Azure Blob Storage and Azure Data Lake Storage. HDFS supports transparent encryption. Apache warns that without Kerberos caller authentication, anyone with network access to cluster servers can access data and run code, and recommends securing clusters before production. Apache also recommends checking release downloads with GPG signatures or SHA-512 checksums. The listed stable release is Hadoop 3.5.0, dated April 2, 2026.

Unilake is an open-source platform for integrating, managing, governing, and querying data across a unified lakehouse. It supports more than 350 pre-built connectors, plus custom integrations built with Unilake, Airbyte, or Meltano SDKs, including a low-code option. Loads can be full or incremental, with streaming ingestion, API flattening, privacy transformations, and SCD type 2 tables. Its query interface can combine Iceberg, Delta, Hudi, Kudu, and Paimon data; analytics use StarRocks and support SQL, object storage, and REST API endpoints. Management features include cataloging, search, lineage, data-quality checks, and automated classification. Governance includes attribute-based and context-aware controls, policies as code, and just-in-time access. Unilake supports SAML 2.0 or OpenID Connect, SCIM provisioning, exportable audit logs, and customer-managed keys. Deployment options range from public, private, and hybrid cloud to on-premises, with Kubernetes and storage among the minimum requirements. The project is in active development and working toward an Alpha release; its AI/ML product is on the roadmap. It is licensed under AGPL 3.0 and EUPL 1.2.