Architecture Data Engineering Systems Tuning 
Dynatrace
JULY 6, 2022
As organizations continue to adopt multicloud strategies, the complexity of these environments grows, increasing the need to automate cloud engineering operations to ensure organizations can enforce their policies and architecture principles. By tuning workflows, you can increase their efficiency and effectiveness.
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. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).
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Abhishek Tiwari
JULY 12, 2018
Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.
Abhishek Tiwari
JULY 20, 2019
Store engineering squad - focus on software and systems required for the storefront including point-of-sales system, promotions, etc. ERP engineering squad - supply chain planning, purchase order management, product lifecycle management, merchandise planning, etc. Secondly, fine-tune team composition based on work.
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. Use cases We found several use cases where a system like AutoOptimize can bring tons of value. Orient: Gather tuning parameters for a particular table that changed.
The Netflix TechBlog
OCTOBER 18, 2022
Due to its popularity, the number of workflows managed by the system has grown exponentially. The scheduler on-call has to closely monitor the system during non-business hours. Meson was based on a single leader architecture with high availability. Meson was based on a single leader architecture with high availability.
The Netflix TechBlog
MAY 26, 2020
And in order to gain visibility into these logs, we need to somehow ingest and enrich this data. It is easier to tune a large Spark job for a consistent volume of data. In other words, we are able to ensure that our Spark app does not “eat” more data than it was tuned to handle. We named this library Sqooby.
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