Co-authors: Khai Tran and Steve Weiss Batch and streaming computations are often combined together in the Lambda architecture, but carry the cost of maintaining two different code bases for the same logic. We have previously shared on the blog a behind-the-scenes look at our approach into enabling the seamless translation of declarative batch code into streaming...
Pinot Articles
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- Topics:
- Stream Processing,
- batch processing,
- Data,
- Pinot,
- Gobblin,
- Kafka,
- Samza
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Co-authors: Timothy Santos and Jeremy Lwanga LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously. One way we’ve worked toward this goal is through LinkedIn Talent Insights, which first launched in 2018. This is a tool that helps organizations...
- Topics:
- Pinot,
- Product Design
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Co-authors: Madhumita Mantri and Tyler James At LinkedIn, ThirdEye is used for business and platform health metrics monitoring, keeping track of a variety of metrics across production infrastructure, AI model performance, or key business indicators (i.e., page view or click count). It’s a key quality assurance system for two reasons: its rule- or model-based...
- Topics:
- artificial intelligence,
- monitoring,
- Pinot,
- machine learning,
- metrics,
- Data
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Built at LinkedIn, Pinot is an open source, distributed, and scalable OLAP data store that we use as our de-facto near-real-time...
- Topics:
- Pinot,
- metrics,
- Data,
- Open Source
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Co-authors: Yen-Jung Chang, Yang Yang, Xiaohui Sun, and Tie Wang At LinkedIn, ThirdEye is the backbone of our monitoring toolkit. We...
- Topics:
- monitoring,
- Pinot,
- metrics,
- Data
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Pinot, an open source scalable distributed columnar OLAP data store, is used extensively across almost all areas of LinkedIn. It...
- Topics:
- Performance,
- Pinot,
- linux,
- Data