The Netflix TechBlog
MARCH 4, 2024
Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).
DZone
AUGUST 16, 2023
From a data engineer's point of view, financial risk management is a series of data analysis activities on financial data. The financial sector imposes its unique requirements on data engineering. Before they adopted an OLAP engine, they were using Kettle to collect data.
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InfoQ
JULY 14, 2023
Mei-Chin Tsai, Vinod discuss the internal architecture of Azure Cosmos DB and how it achieves high availability, low latency, and scalability. By Mei-Chin Tsai, Vinod Sridharan
InfoQ
APRIL 15, 2024
He specifically delved into Venice DB, the NoSQL data store used for feature persistence. At the QCon London 2024 conference, Félix GV from LinkedIn discussed the AI/ML platform powering the company’s products. By Rafal Gancarz
The Netflix TechBlog
MARCH 7, 2024
Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding.
Abhishek Tiwari
JULY 12, 2018
Building data pipelines can offer strategic advantages to the business. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines. Data pipeline initiatives are generally unfinished projects. In this post, we will discuss why you should avoid building data pipelines in first place.
Abhishek Tiwari
DECEMBER 27, 2017
Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Key challenges. Performance.
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