The Netflix TechBlog
OCTOBER 28, 2021
Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix.
The Netflix TechBlog
MARCH 5, 2019
This freedom allows teams and individuals to move fast to deliver on innovation and feel responsible for quality and robustness of their delivery. All these micro-services are currently operated in AWS cloud infrastructure. In the next section, we will highlight some high level areas of focus in each dimension of our infrastructure.
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Dynatrace
JULY 6, 2022
With ever-evolving infrastructure, services, and business objectives, IT teams can’t keep up with routine tasks that require human intervention. This requires significant data engineering efforts, as well as work to build machine-learning models. How organizations benefit from automating IT practices.
The Netflix TechBlog
MARCH 2, 2021
At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.
Adrian Cockcroft
FEBRUARY 13, 2023
STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager Data Engineering and Michael Floyd AWS Head of Sustainability Solutions. SUS312 How innovators are driving more sustainable manufacturing — Marcus Ulmefors Northvolt Director Data and ML Platforms and Muhammad Sajid AWS SA.
The Netflix TechBlog
MARCH 25, 2019
Building and Scaling Data Lineage at Netflix to Improve Data Infrastructure Reliability, and Efficiency By: Di Lin , Girish Lingappa , Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question?—?“Can
O'Reilly
FEBRUARY 18, 2020
This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Software architecture, infrastructure, and operations are each changing rapidly. Also: infrastructure and operations is trending up, while DevOps is trending down.
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