The LinkedIn infrastructure has thousands of services serving millions of queries per second. At this scale, having tools that provide observability into the LinkedIn infrastructure is imperative to ensure that issues in our infrastructure are quickly detected, diagnosed, and remediated. This level of visibility helps prevent the occurrence of outages so we can...
Pinot Articles
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At LinkedIn, Apache Kafka is used heavily to store all kinds of data, such as member activity, log storage, metrics storage, and a multitude of inter-service messaging. LinkedIn maintains multiple data centers with multiple Kafka clusters per data center, each of which contains an independent set of data. Mirroring (i.e., replicating) Kafka topics across the...
- Topics:
- Pinot,
- Kafka,
- Open Source
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Co-authors: Siddharth Teotia and Tim Santos Introduction LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph...
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Co-authors: Vincent Wang, Siddharth Teotia, Manoj Thakur, and Mayank Shrivastava As our LinkedIn Marketing Solutions Blog recently...
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Co-authors: Xiang Zhang and Jingyu Zhu Introduction The Lambda architecture has become a popular architectural style that promises...
- Topics:
- Stream Processing,
- Pinot,
- Profile,
- Architecture,
- Kafka,
- batch processing
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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