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MezzFS?—?Mounting object storage in Netflix’s media processing platform

The Netflix TechBlog

Mounting object storage in Netflix’s media processing platform By Barak Alon (on behalf of Netflix’s Media Cloud Engineering team) MezzFS (short for “Mezzanine File System”) is a tool we’ve developed at Netflix that mounts cloud objects as local files via FUSE. Our object storage service splits objects into many parts and stores them in S3.

Media 218
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Introducing Netflix’s Key-Value Data Abstraction Layer

The Netflix TechBlog

These include challenges with tail latency and idempotency, managing “wide” partitions with many rows, handling single large “fat” columns, and slow response pagination. It also serves as central configuration of access patterns such as consistency or latency targets.

Latency 260
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Title Launch Observability at Netflix Scale

The Netflix TechBlog

The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? Additionally, the time-sensitive nature of these investigations precludes the use of cold storage, which cannot meet the stringent SLAs required.

Traffic 172
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Nine ways technology executives can get significant business value with the right observability platform

Dynatrace

I also have the privilege of being “customer zero” for our platform, which enables me to continually discover where Dynatrace can deliver on more use cases to drive my team’s productivity and innovation. That’s because it does not require any pre-prepared schemas, and access to cold/hot storage is fully automatic and with zero latency.

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Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

By Xiaomei Liu , Rosanna Lee , Cyril Concolato Introduction Behind the scenes of the beloved Netflix streaming service and content, there are many technology innovations in media processing. Uploading and downloading data always come with a penalty, namely latency. Packaging has always been an important step in media processing.

Cloud 242
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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.

Latency 239
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Analyze OpenTelemetry traces and log data at scale: Accelerate troubleshooting and optimize application performance

Dynatrace

Teams need a better way to work together, eliminate silos and spend more time innovating. Without distributed tracing, pinpointing the cause of increased latency could take hours or even days. There is no need to think about schema and indexes, re-hydration, or hot/cold storage. The same is true when it comes to log ingestion.