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3. Psyberg: Automated end to end catch up

The Netflix TechBlog

In the previous installments of this series, we introduced Psyberg and delved into its core operational modes: Stateless and Stateful Data Processing. Pipelines After Psyberg Let’s explore how different modes of Psyberg could help with a multistep data pipeline. In this case, the minimum hour to process the data is hour 2.

Tuning 244
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What is IT automation?

Dynatrace

At its most basic, automating IT processes works by executing scripts or procedures either on a schedule or in response to particular events, such as checking a file into a code repository. Adding AIOps to automation processes makes the volume of data that applications and multicloud environments generate much less overwhelming.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

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. In this way, no human intervention is required in the remediation process. Multi-objective optimizations.

Tuning 210
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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

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Friends don't let friends build data pipelines

Abhishek Tiwari

In recent times, in order to gain valuable insights or to develop the data-driven products companies such as Netflix, Spotify, Uber, AirBnB have built internal data pipelines. If built correctly, data pipelines can offer strategic advantages to the business. Depending on frameworks, data processing units (a.k.a

Latency 63
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Optimizing data warehouse storage

The Netflix TechBlog

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Some of the optimizations are prerequisites for a high-performance data warehouse.

Storage 203
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Analytics at Netflix: Who we are and what we do

The Netflix TechBlog

The Engineer enjoys making data available by piping it in from new sources in optimal ways, building robust data models, prototyping systems, and doing project-specific engineering.

Analytics 240