Apache Hadoop HDFS vs lakeFS

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.

lakeFS provides data version control and governance for teams working with petabyte-scale multimodal data. Its engine records changes and uses branches, commits, merges, and rollbacks to manage data versions. Branches create isolated environments without copying data or adding storage costs. Write-Audit-Publish workflows use automated hooks and human review to check changes before merging. Immutable commits, tags, and lineage connect experiments and agent actions to the data versions behind them. Listed controls include role-based access, geo-based policies, data immutability, SSO, and SCIM; lakeFS states that it is SOC 2 compliant and provides audit trails and lineage. It works with AWS S3, Azure Blob, Google Cloud Storage, and on-premises object stores such as MinIO. Integrations include Spark, Trino, DuckDB, Databricks, MLflow, SageMaker, Vertex AI, Kubeflow, Airflow, and Airbyte. Enterprise can run as managed lakeFS Cloud or as a self-managed deployment on-premises, in a private cloud, or in an air-gapped environment. Pricing is on request, and a 15-day trial includes full Enterprise access and support from lakeFS experts.