A distributed data integration framework that simplifies common aspects of big data integration such as data ingestion, replication, organization and lifecycle management for both streaming and batch data ecosystems.

Clone this repo:
  1. 1367614 [GOBBLIN-1497] Reduce the number of calls on FlowSpec initialization where possible,… (#3340) by William Lo · 2 days ago master
  2. 9a9e239 [GOBBLIN-1496] Initialize arrays and maps related to gobblin as a service with an in… (#3339) by William Lo · 2 days ago
  3. e9b7eac [GOBBLIN-1495] Fix NPE when trying to fetch Hadoop tokens for cluster with no remote namenodes (#3341) by Dushyant Kumar · 2 days ago
  4. c65e5d0 [GOBBLIN-1491] Add option to mark up/down D2 servers for gobblin service on leadership change (#3333) by Jack Moseley · 8 days ago
  5. 48af4c6 [GOBBLIN-1494]Fix flaky test on HiveSource (#3338) by William Lo · 8 days ago

Apache Gobblin

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Apache Gobblin is a highly scalable data management solution for structured and byte-oriented data in heterogeneous data ecosystems.


  • Ingestion and export of data from a variety of sources and sinks into and out of the data lake. Gobblin is optimized and designed for ELT patterns with inline transformations on ingest (small t).
  • Data Organization within the lake (e.g. compaction, partitioning, deduplication)
  • Lifecycle Management of data within the lake (e.g. data retention)
  • Compliance Management of data across the ecosystem (e.g. fine-grain data deletions)


  • Battle tested at scale: Runs in production at petabyte-scale at companies like LinkedIn, PayPal, Verizon etc.
  • Feature rich: Supports task partitioning, state management for incremental processing, atomic data publishing, data quality checking, job scheduling, fault tolerance etc.
  • Supports stream and batch execution modes
  • Control Plane (Gobblin-as-a-service) supports programmatic triggering and orchestration of data plane operations.

Common Patterns used in production

  • Stream / Batch ingestion of Kafka to Data Lake (HDFS, S3, ADLS)
  • Bulk-loading serving stores from the Data Lake (e.g. HDFS -> Couchbase)
  • Support for data sync across Federated Data Lake (HDFS <-> HDFS, HDFS <-> S3, S3 <-> ADLS)
  • Integrate external vendor API-s (e.g. Salesforce, Dynamics etc.) with data store (HDFS, Couchbase etc)
  • Enforcing Data retention policies and GDPR deletion on HDFS / ADLS

Apache Gobblin is NOT

  • A general purpose data transformation engine like Spark or Flink. Gobblin can delegate complex-data processing tasks to Spark, Hive etc.
  • A data storage system like Apache Kafka or HDFS. Gobblin integrates with these systems as sources or sinks.
  • A general-purpose workflow execution system like Airflow, Azkaban, Dagster, Luigi.


  • Java >= 1.8

If building the distribution with tests turned on:

  • Maven version 3.5.3

Instructions to run Apache RAT (Release Audit Tool)

  1. Extract the archive file to your local directory.
  2. Run ./gradlew rat. Report will be generated under build/rat/rat-report.html

Instructions to build the distribution

  1. Extract the archive file to your local directory.
  2. Skip tests and build the distribution: Run ./gradlew build -x findbugsMain -x test -x rat -x checkstyleMain The distribution will be created in build/gobblin-distribution/distributions directory. (or)
  3. Run tests and build the distribution (requires Maven): Run ./gradlew build The distribution will be created in build/gobblin-distribution/distributions directory.

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