The Netflix TechBlog
MARCH 25, 2019
We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.
The Netflix TechBlog
OCTOBER 18, 2022
Meson was based on a single leader architecture with high availability. It serves thousands of users, including data scientists, data engineers, machine learning engineers, software engineers, content producers, and business analysts, for various use cases. Figure 1 shows the high-level architecture.
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The Netflix TechBlog
DECEMBER 21, 2020
This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture. Some of the optimizations are prerequisites for a high-performance data warehouse. Both automatic (event-driven) as well as manual (ad-hoc) optimization.
O'Reilly
JULY 15, 2020
Software engineers comprise the survey audience’s single largest cluster, over one quarter (27%) of respondents (Figure 1). If you combine the different architectural roles—i.e., Adding architects and engineers, we see that roughly 55% of the respondents are directly involved in software development.
The Netflix TechBlog
MARCH 5, 2019
While our engineering teams have and continue to build solutions to lighten this cognitive load (better guardrails, improved tooling, …), data and its derived products are critical elements to understanding, optimizing and abstracting our infrastructure. Give us a holler if you are interested in a thought exchange.
InfoQ
DECEMBER 29, 2023
Zendesk reduced its data storage costs by over 80% by migrating from DynamoDB to a tiered storage solution using MySQL and S3. The company considered different storage technologies and decided to combine the relational database and the object store to strike a balance between querybility and scalability while keeping the costs down.
Abhishek Tiwari
DECEMBER 27, 2017
ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. There are several emerging data trends that will define the future of ETL in 2018. Unified data management architecture.
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