Co-authors: Krishnan Raman and Joey Salacup Monitoring big data pipelines often equates to waiting for a long-running batch job to complete and observing the status of the execution. The status can result in “Failed” or “Successful” or even “Incomplete.” From there, it’s the team’s job to understand the impact and troubleshoot the situation to identify a...
Gobblin Articles
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- Topics:
- infrastructure,
- Gobblin,
- tools,
- Kafka
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Gobblin is a distributed data integration framework that simplifies common aspects of big data integration, such as ingestion, replication, organization, and lifecycle management, for both streaming and batch ecosystems. Gobblin has been gobbling big data with ease in the open source world since December 2014. Over the years, Gobblin has evolved at a tremendous...
- Topics:
- Stream Processing,
- Big Data,
- Gobblin,
- Open Source
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About a year ago, we open sourced Gobblin, a universal data ingestion framework that aimed to solve data integration challenges faced by people working on big data problems. We have described how LinkedIn is using Gobblin to ingest data at massive scale from a variety of sources to HDFS, in many previous blog posts, publications, and talks. Today, we are very...
- Topics:
- Hadoop,
- Big Data,
- Open Source,
- Data Ingestion,
- Distributed Systems,
- ETL,
- Gobblin,
- Kafka
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We shared Gobblin with the open source community a year ago. Since then, we’ve seen increasing interest and adoption among engineers,...
- Topics:
- Hadoop,
- Data Ingestion,
- Gobblin,
- Kafka,
- Open Source
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Genesis Less than a year ago, we introduced Gobblin, a unified ingestion framework, to the world of Big Data. Since then, we’ve shared...
- Topics:
- Big Data,
- Hadoop,
- Open Source,
- Distributed Systems,
- ETL,
- Gobblin,
- Kafka
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When dealing with massive amounts of data at scale, it’s important to have state of the art infrastructure and algorithms that can...
- Topics:
- Pinot,
- data analytics,
- Cubert,
- infrastructure,
- Gobblin