Co-authors: Zihan Li, Sudarshan Vasudevan, Lei Sun, and Shirshanka Das Data analytics and AI power many business-critical use cases at LinkedIn. We need to ingest data in a timely and reliable way from a variety of sources, including Kafka, Oracle, and Espresso, bringing it into our Hadoop data lake for subsequent processing by AI and data science pipelines. We...
Stream Processing Articles
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- Topics:
- Stream Processing,
- Hadoop,
- Data,
- batch processing,
- Open Source,
- Gobblin,
- Kafka
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Co-authors: Xiang Zhang and Jingyu Zhu Introduction The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods. But it also has some drawbacks, such as complexity and additional development/operational overheads. One of...
- Topics:
- Stream Processing,
- Pinot,
- Profile,
- Architecture,
- Kafka,
- batch processing
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Co-authors: Yixing Zhang, Bingfeng Xia, Ke Wu, and Xinyu Liu Since Beam Samza runner was developed in 2018 at LinkedIn, we now have 100+ Samza Beam jobs running in production. As our usage grew, we wanted to better understand how the Samza runner performs compared to other runners and identify areas of improvement. In general, for stream processing platforms,...
- Topics:
- Stream Processing,
- Apache Samza,
- Performance,
- Benchmark
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Co-authors: Khai Tran and Steve Weiss Batch and streaming computations are often combined together in the Lambda architecture, but...
- Topics:
- Stream Processing,
- batch processing,
- Data,
- Pinot,
- Gobblin,
- Kafka,
- Samza
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Editor's note: This blog has been updated. Brooklin—a distributed service for streaming data in near real-time and at scale—has been...
- Topics:
- scale,
- Stream Processing,
- infrastructure,
- Kafka,
- Data,
- Open Source
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If Apache Kafka is the lifeblood of all nearline processing at LinkedIn, then Apache Samza is the beating heart pumping that blood...
- Topics:
- Stream Processing,
- Testing,
- Samza