
Trino
Fast distributed SQL query engine for big data analytics.

Enterprise-grade data governance and metadata management for hybrid-cloud ecosystems.

Apache Atlas is a scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and the broader modern data stack. As of 2026, Atlas remains the industry standard for open-source metadata management, leveraging a graph-based metadata store powered by Apache JanusGraph and Apache Solr for high-performance indexing. Its architecture is designed to provide a common metadata framework that allows for the exchange of metadata between different tools and platforms. By utilizing a robust 'Hooks' system, it captures lineage from processing engines like Spark, Hive, and Sqoop in real-time. In a 2026 market context, Atlas serves as the critical 'Source of Truth' for AI-ready data, ensuring that large language models (LLMs) and automated pipelines ingest only verified, governed, and tagged data assets. It facilitates deep cross-platform data discovery and lineage, supporting complex regulatory environments like GDPR, CCPA, and the EU AI Act by providing clear visibility into data provenance and transformation history.
Apache Atlas is a scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and the broader modern data stack.
Explore all tools that specialize in track data lineage. This domain focus ensures Apache Atlas delivers optimized results for this specific requirement.
Explore all tools that specialize in data lineage tracking. This domain focus ensures Apache Atlas delivers optimized results for this specific requirement.
Automatically propagates tags from parent entities to child entities across the lineage graph.
Maintains a history of metadata changes for every entity, allowing for point-in-time governance audits.
Stitches together lineage from disparate systems like Sqoop, Hive, and Spark into a unified graph.
Allows users to define custom metadata types and relationships via JSON-based specifications.
Native hook for tag-based security policies that dynamically control access based on Atlas metadata.
Captures all metadata modifications and access events into a centralized audit store.
Full-text search capabilities across complex attributes and relationships using the Solr backend.
Provision a Java 11+ environment with adequate heap memory (8GB minimum recommended).
Configure a graph repository backend using Apache HBase or Cassandra.
Set up Apache Solr for full-text search and indexing of metadata entities.
Download the latest Apache Atlas distribution and extract the binaries.
Configure 'atlas-application.properties' to define backend storage and indexing URLs.
Initialize the Atlas metadata model using the 'atlas_start.py' script.
Deploy Atlas Hooks into source systems like Apache Spark, Hive, or Kafka.
Access the Web UI via port 21000 to verify initial entity ingestion.
Configure Apache Ranger integration for classification-based access control.
Run the first metadata sync to populate the Business Glossary and Lineage maps.
All Set
Ready to go
Verified feedback from other users.
"Users praise its comprehensive lineage and deep integration with the Hadoop ecosystem, though some note a steep learning curve for initial setup and configuration."
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Fast distributed SQL query engine for big data analytics.

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