Availability Big Data Data Engineering Tuning 
The Netflix TechBlog
JULY 21, 2022
More importantly, the low resource availability or “out of memory” scenario is one of the common reasons for crashes/kills. We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform.
The Netflix TechBlog
MAY 26, 2020
Network Availability: The expected continued growth of our ecosystem makes it difficult to understand our network bottlenecks and potential limits we may be reaching. And in order to gain visibility into these logs, we need to somehow ingest and enrich this data. It is easier to tune a large Spark job for a consistent volume of data.
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The Netflix TechBlog
MARCH 25, 2019
We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.
The Netflix TechBlog
OCTOBER 18, 2022
by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.
The Netflix TechBlog
DECEMBER 21, 2020
Some of the optimizations are prerequisites for a high-performance data warehouse. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster. Orient: Gather tuning parameters for a particular table that changed.
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