Co-authors: Xiang Zhang and Chuck Jerian Espresso is LinkedIn’s document-oriented, highly available, and timeline-consistent distributed datastore. Clients, such as services fetching profile data, read a subset fields of whole documents from Espresso in different use cases for reasons related to performance and costs. For example, one use case is reading only a...
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- Topics:
- Performance,
- Data,
- ESPRESSO
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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 use it to keep track of a variety of metrics, whether it be related to production infrastructure and AI model performance, or business impact, such as page view or click count. It’s a key quality assurance system because it...
- Topics:
- monitoring,
- Pinot,
- metrics,
- Data
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Co-authors: Kerem Sahin, Mars Lan, and Shirshanka Das Finding the right data quickly is critical for any company that relies on big data insights to make data-driven decisions. Not only does this impact the productivity of data users (including analysts, machine learning developers, data scientists, and data engineers), but it also has a direct impact on end...
- Topics:
- Metadata,
- infrastructure,
- Data,
- Open Source
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Editor's Note: LinkedIn Engineering is dedicated to solving complex problems at scale to create economic opportunity for every member...
- Topics:
- engineering culture,
- Women in Tech,
- NYC Engineering,
- SRE
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Nearly 20 years after the first release of Python 2 and 11 years after the first release of Python 3, the Python development community...
- Topics:
- Python,
- Open Source
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Our members place their trust in us, and expect and deserve a safe and trusted community where they can express themselves...