Apache Ozone vs HPCC Systems

Apache Ozone is a free, self-hosted distributed object store for data lakes, lakehouse workloads, AI/ML, cloud-native applications, and Hadoop or Spark storage. It supports deployments reaching billions of objects and exabytes of capacity, with configurable replication or erasure coding for each bucket or object. Applications can connect through an S3-compatible REST API, a Hadoop-compatible filesystem interface, a Java client API, or command-line tools. Ozone provides strong consistency: reads reflect the latest successfully completed writes. Security options include Kerberos authentication, ACLs, Ranger policy management, TLS/SSL in transit, and TDE at rest. Administrators can set volume and bucket quotas, create read-only point-in-time bucket snapshots, and use the Recon web UI or CLI tools. Listed integrations include Hive, Spark, Flink, Iceberg, Trino, Impala, Ranger, Knox, Kerberos, Prometheus, Grafana, Oozie, Hue, HBase, and DistCp. Deployment guides cover Docker, Kubernetes, and bare metal. The software is licensed under Apache License 2.0; the Apache Software Foundation says it does not provide formal or commercial support.

HPCC Systems is a free, open-source platform for enterprise big-data processing and data-lake management. Its core components are ECL, a declarative and modular language, Thor for data refinement, and Roxie for data delivery. ECL's compiler optimizes programs for parallel processing and compiles them into C++. Thor handles ingestion, transformation, linking, and indexing; Roxie serves concurrent queries. Clusters can scale from two computers to more than a thousand commodity-hardware nodes. The cloud-native platform runs on Kubernetes and supports Azure Kubernetes Service and Amazon Elastic Kubernetes Service. Storage options include AWS S3, Azure Blob Storage, AWS Elastic Block Store, Azure Files, and Azure Disks. Security capabilities include end-to-end encryption, service meshes, OAuth 2.0 with Azure AD support, JWT, and configurable controls for data and services. The Machine Learning Library offers ECL-accessible algorithms for building and testing prediction models. Integrations include Spark, Kafka, Couchbase, Redis, Memcached, Pentaho, R, JDBC, Java APIs, and Tableau data connectors. ESP exposes queries through XML, HTTP, SOAP, and REST interfaces, and deployed queries can be called with REST/JSON. The platform is free to use under the Apache 2.0 license and is self-hosted.