LinkedIn delivers value to more than 660 million members via thousands of microservices, most of which depend on data infrastructure or mid-tier infrastructure platforms. This means that in order to launch a new application, developers traditionally had to request and set up an online database as a business data source of truth or stream data via Kafka topics,...
Data Articles
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- Topics:
- infrastructure,
- Data
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With more than a half billion members on LinkedIn, we have had to create new ways to scale our infrastructure and support the tremendous growth in data. We’re always looking for the best ways to manage our growth, from our internally-built systems to leveraging technology from Azure for external cloud integrations. One of the ways we manage this data influx is...
- Topics:
- infrastructure,
- Data
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In the era of big data, corporations and businesses are increasingly collecting immense amounts of unstructured data in the form of free text, from customer service conversations to market research surveys. While it is clear that such member feedback, or “Voice of the Member” (VOM), contains valuable information, it is often less clear how to best analyze such...
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In modern data-driven businesses, the complexity that arises from fast-paced analytics, data mining and ETL processes makes metadata...
- Topics:
- Data,
- Open Source,
- WhereHows
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Update Apr 13, 2016: There are numerous improvement to Samza cachestore (SAMZA-658, SAMZA-812, SAMZA-873 etc.) since our last test...
- Topics:
- Stream Processing,
- Apache Samza,
- Performance,
- performance analysis,
- Kafka,
- Data
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Apache Samza has been run in production and is used by many LinkedIn services to solve a variety of stream processing scenarios. For...
- Topics:
- Performance,
- Apache Samza,
- Stream Processing,
- performance analysis,
- Kafka,
- Data