The Netflix TechBlog

MARCH 4, 2024

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

Tuning Efficiency Big Data Engineering 210

Percona

JANUARY 19, 2023

Compression: Compression is the process of restructuring the data by changing its encoding in order to store it in fewer bytes. There are many compression tools and algorithms for data out there. It was developed for optimizing data storage and access for big data sets. 1 mysql mysql 592K Dec 30 02:48 tb1.ibd

Open Source 84

Open Source Storage Database Big Data 84

The Netflix TechBlog

JULY 26, 2021

Netflix is known for its loosely coupled microservice architecture and with a global studio footprint, surfacing and connecting the data from microservices into a studio data catalog in real time has become more important than ever. With the latest Data Mesh Platform, data movement in Netflix Studio reaches a new stage.

Big Data 253

Big Data Government Analytics Processing 253

Dynatrace

APRIL 20, 2023

-based financial services group, discussed how the bank uses log monitoring on the Dynatrace platform with an emphasis on observability and security data. To grasp the challenges of multifeatured, cross-team cooperation dealing with observability data, consider the content of the logs generated. Dissolving data silos.

Analytics 188

Analytics Infrastructure Storage Efficiency 188

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.

Java 202

Java Scalability Traffic Architecture 202

The Netflix TechBlog

DECEMBER 21, 2020

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

Storage Latency Efficiency Data Engineering 203

The Netflix TechBlog

MARCH 25, 2019

In addition, we derive lineage information from scheduled ETL jobs by extracting workflow definitions and runtime metadata using Meson scheduler APIs. Netflix’s diverse data landscape made it challenging to capture all the right data and conforming it to a common data model.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219