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Optimizing data warehouse storage

The Netflix TechBlog

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 214
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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 253
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Introducing Netflix TimeSeries Data Abstraction Layer

The Netflix TechBlog

Rajiv Shringi Vinay Chella Kaidan Fullerton Oleksii Tkachuk Joey Lynch Introduction As Netflix continues to expand and diversify into various sectors like Video on Demand and Gaming , the ability to ingest and store vast amounts of temporal data — often reaching petabytes — with millisecond access latency has become increasingly vital.

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Edgar: Solving Mysteries Faster with Observability

The Netflix TechBlog

Edgar helps Netflix teams troubleshoot distributed systems efficiently with the help of a summarized presentation of request tracing, logs, analysis, and metadata. Telltale provides Edgar with latency benchmarks that indicate if the individual trace’s latency is abnormal for this given service. What is Edgar?

Latency 299
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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.

Storage 130
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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

While Atlas is architected around compute & storage separation, and we could theoretically just scale the query layer to meet the increased query demand, every query, regardless of its type, has a data component that needs to be pushed down to the storage layer.

Storage 300
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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

The data warehouse is not designed to serve point requests from microservices with low latency. Therefore, we must efficiently move data from the data warehouse to a global, low-latency and highly-reliable key-value store. As most key-value storage engines support efficiently deleting a namespace (e.g.

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