Cloudera Data Lake Service vs Unilake

Cloudera Data Lake Service provides data governance, metadata management, access controls, and auditing for data lake workloads. It captures schema and metadata definitions that platform workloads discover or create, then supports search, lineage, chain-of-custody auditing, classification, profiling, and a business glossary. Security capabilities include role- and attribute-based policies, auditing of unauthorized access, and encryption at rest and in motion with key management. Apache Ranger can apply access policies at database, table, column, and file levels, including dynamic conditions such as time or geolocation. Apache Knox provides a protected single sign-on gateway to Data Lake and Cloudera Data Platform interfaces. A cluster includes Hive Metastore, Ranger, Atlas, and Knox. Cloudera supports light-duty and enterprise Data Lakes on AWS, Azure, and GCP. Listed capabilities also include SQL analytics, streaming ingestion, data sharing, and open table formats. The service has web and API access and supports hybrid deployment. The listed pricing note starts at $0.20, while the plan is also described as Contact Sales for pricing; cloud prices exclude infrastructure and networking costs.

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.