Co-authors: Sandhya Ramu and Vasanth Rajamani For companies and organizations, failure tends to be far more illuminating than success and the lingering effects of a failure can be harmful if the team moves too quickly and does not resolve the issue in a thorough and transparent manner. We recently ran into a large incident that involved data loss in our big data...
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- Topics:
- Performance,
- Hadoop,
- data center,
- Data
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Co-authors: Konstantin Salomatin, Kirill Talanine, Barış Özmen, Gungor Polatkan, Linda Fayad, Arjun Kulothungun, and Deepak Kumar Our vision at LinkedIn is to create economic opportunity for every member of the global workforce. Key to achieving this is making the marketplace between job seekers and hirers more efficient. Active job seekers apply for many jobs,...
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Co-authors: Weiwei Guo, Xiaowei Liu, Sida Wang, Huiji Gao, and Bo Long Natural language processing (NLP) technologies are widely deployed to process rich natural language text data for search and recommender systems. Achieving high-quality search and recommendation results requires that information, such as user queries and documents, be processed and understood...
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Co-authors: Scott Meyer, Andrew Carter, and Andrew Rodriguez Editor’s note: In this two-part blog series, we introduce LIquid, a new...
- Topics:
- scale,
- infrastructure,
- knowledge graph,
- Data
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Co-authors: Pradhan Cadabam and Jingxuan (Rex) Zhang Messaging has been a core part of Linkedin since the day we launched and our...
- Topics:
- infrastructure,
- Architecture,
- Product Design
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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