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Fast distributed SQL query engine for big data analytics.

Declarative data governance and pipeline management for the Hadoop ecosystem.

Apache Falcon is a specialized data governance engine designed to manage the data lifecycle within the Apache Hadoop ecosystem. As a high-level framework, it allows users to define, schedule, and monitor data management policies through a declarative XML-based interface. Its technical architecture focuses on decoupling the data pipeline logic from the underlying execution engines, primarily leveraging Apache Oozie for workflow orchestration. While Apache Falcon moved to the Apache Attic in June 2019, its methodologies remain foundational for 2026 DataOps practices, specifically regarding cross-cluster data replication, data lineage tracking, and automated data retention policies. It provides a centralized registry for data entities—Clusters, Feeds, and Processes—enabling large-scale enterprises to maintain compliance and auditing standards across distributed HDFS environments. Its core value proposition in a 2026 context is for legacy Hadoop management and as a blueprint for metadata-driven automation in hybrid cloud environments. Falcon handles complex tasks such as 'late data handling' and disaster recovery synchronization, ensuring data consistency across geographically dispersed data centers without manual intervention.
Apache Falcon is a specialized data governance engine designed to manage the data lifecycle within the Apache Hadoop ecosystem.
Explore all tools that specialize in orchestrate data pipelines. This domain focus ensures Apache Falcon delivers optimized results for this specific requirement.
Explore all tools that specialize in track data lineage. This domain focus ensures Apache Falcon delivers optimized results for this specific requirement.
Explore all tools that specialize in lineage tracking. This domain focus ensures Apache Falcon delivers optimized results for this specific requirement.
Enables the definition of policies to handle data that arrives after the scheduled processing window has closed.
Uses DistCp to move data between different HDFS clusters based on feed definitions.
Allows users to specify TTL (Time To Live) for data feeds, automatically cleaning up old HDFS data.
Associates metadata tags with data entities for easier search and governance.
Generates graphical representations of data flow from source to destination clusters.
Translates high-level Falcon processes into low-level Oozie workflow XMLs automatically.
Provides a RESTful interface to query the status of all instances (succeeded, failed, running).
Verify Hadoop cluster availability (HDFS, Hive, and Oozie must be running).
Download the Apache Falcon binary distribution from the Apache Attic archive.
Configure 'falcon-env.sh' to set JAVA_HOME and FALCON_LOG_DIR.
Update 'startup.properties' to define the Berkeley DB storage location for metadata.
Start the Falcon server using the 'falcon-start' script.
Define a 'Cluster Entity' in XML to register your HDFS and Oozie endpoints.
Define a 'Feed Entity' to specify data locations, frequency, and retention policies.
Define a 'Process Entity' to link inputs, outputs, and the transformation logic (Pig/Hive).
Submit and schedule the entities using the Falcon CLI command 'falcon entity -submit -type [type] -file [xml]'.
Monitor pipeline health and lineage via the Falcon Web UI or REST API.
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