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Friends don't let friends build data pipelines

Abhishek Tiwari

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Latency 63
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QCon London: Lessons Learned From Building LinkedIn’s AI/ML Data Platform

InfoQ

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

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Optimizing data warehouse storage

The Netflix TechBlog

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.

Storage 203
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

Whether in analyzing A/B tests, optimizing studio production, training algorithms, investing in content acquisition, detecting security breaches, or optimizing payments, well structured and accurate data is foundational. Users configure the workflow to read the data in a window (e.g. data arrives too late to be useful).