Architecture Data Engineering Open Source Performance 
The Netflix TechBlog
OCTOBER 18, 2022
Meson was based on a single leader architecture with high availability. Therefore, the orchestrator has to manage a workflow consisting of hundreds of thousands of jobs in a performant way, which is also quite challenging. Figure 1 shows the high-level architecture. With the high growth of workflows in the past few years?
The Netflix TechBlog
MARCH 5, 2019
As a micro-service owner, a Netflix engineer is responsible for its innovation as well as its operation, which includes making sure the service is reliable, secure, efficient and performant. In the Performance space, our data teams currently focus on the quality of experience on Netflix-enabled devices.
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The Symphonia
JUNE 19, 2018
We’ve got you covered), partnering with friends on a fascinating open source project, our most comprehensive training course yet, plenty of speaking engagements (see our expanding portfolio of talks that we can give to your team here ), and a revisit of an old friend. John and I (Mike) have had a fun three months.
Abhishek Tiwari
JULY 20, 2019
For instance, if you are fast-growing VC funded e-commerce startup and your number one business priority is multiplying current growth and performing exceptionally well on key financial metrics charted out by your investors. In this model, software architecture and code ownership is a reflection of the organisational model.
Abhishek Tiwari
DECEMBER 27, 2017
Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Performance.
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
DECEMBER 22, 2017
Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. Then we perform frequent batch ETL from application databases to a data warehouse. Classic ETL. Stateless and elastic.
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