Dynatrace

JULY 6, 2022

IT admins can automate virtually any time-consuming task that requires regular application. This requires significant data engineering efforts, as well as work to build machine-learning models. And what are the best strategies to reduce manual labor so your team can focus on more mission-critical issues? What is IT automation?

Artificial Intelligence 246

Artificial Intelligence Tuning Strategy Big Data 246

O'Reilly

FEBRUARY 18, 2020

Along with R , Python is one of the most-used languages for data analysis. there’s a Python library for virtually anything a developer or data scientist might need to do. Python libraries are no less useful for manipulating or engineering data, too.). In aggregate, data engineering usage declined 8% in 2019.

Software Architecture 145

Software Architecture DevOps Data Engineering Architecture 145

InfoQ

APRIL 7, 2021

Recently, Google announced A2 Virtual Machines (VMs)' general availability based on the NVIDIA Ampere A100 Tensor Core GPUs in Compute Engine.

Virtualization 57

Virtualization Google Availability Engineering 57

The Netflix TechBlog

SEPTEMBER 10, 2024

The first was voice control, where you can play a title or search using your virtual assistant with a voice command like “Show me Stranger Things on Netflix.” (See History & motivation There were two main motivating use cases that drove Pushy’s initial development and usage.

Latency 234

Latency Cache Tuning Efficiency 234

O'Reilly

JULY 15, 2020

Technical roles represented in the “Other” category include IT managers, data engineers, DevOps practitioners, data scientists, systems engineers, and systems administrators. So it just makes sense to instantiate microservices at the level of the virtual machine (VM), as distinct to that of the container.

Database 145

Database Architecture Education Systems 145

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.

Big Data 63

Big Data Storage Benchmarking Hardware 63

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.

Latency 63

Latency Analytics Scalability Engineering 63