Co-Authors: Yuhong Cheng, Shangjin Zhang, Xinyu Liu, and Yi Pan Efficient data processing is crucial in reducing learning curves, simplifying maintenance efforts, and decreasing operational complexity. This, in turn, helps engineers to develop and deploy data processing applications quickly and easily, powering various business requirements, and enhancing member...
Apache Samza Articles
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- Topics:
- Apache Samza,
- Spark,
- Stream Processing,
- apache,
- Data Streams
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Co-authors: Rupesh Gupta, Sasha Ovsankin, Qing Li, Seunghyun Lee, Benjamin Le, and Sunil Khanal At LinkedIn, we strive to serve the most relevant recommendations to our members, whether that’s a job they may be interested in, a member they may want to connect with, or another type of suggestion. In order to do that, we need to know their intent and preferences,...
- Topics:
- Recommendations,
- Apache Samza,
- Recommender Systems
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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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Over a decade ago, test strategies invested heavily in UI-driven tests. Backend and mid-tier services were tested using automated...
- Topics:
- Apache Samza,
- Stream Processing,
- Testing,
- Kafka
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This post is the second in a series discussing asynchronous processing and multithreading in Apache Samza. In the previous post, we...
- Topics:
- Apache Samza,
- Stream Processing,
- Big Data,
- Kafka
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As part of the Apache Samza 0.11 release, we rebuilt Samza’s underlying event processing engine to use an asynchronous and parallel...
- Topics:
- Apache Samza,
- Stream Processing,
- Big Data,
- Kafka