Dynatrace

JANUARY 16, 2023

Java, Go, and Node.js The immense growth of Kubernetes presents new security challenges in runtime and increased complexity in hardening CI/CD pipelines in development. Big data : To store, search, and analyze large datasets, 32% of organizations use Elasticsearch. Java, Go, and Node.js

Open Source 340

Open Source Java Operating System Programming 340

Dynatrace

SEPTEMBER 15, 2020

Stefano started his presentation by showing how much cost and performance optimization is possible when knowing how to properly configure your application runtimes, databases, or cloud environments: Correct configuration of JVM parameters can save up to 75% resource utilization while delivering same or better performance!

Performance 183

Performance Java Metrics Cloud 183

Rigor

JANUARY 13, 2020

Take, for example, The Web Almanac , the golden collection of Big Data combined with the collective intelligence from most of the authors listed below, brilliantly spearheaded by Google’s @rick_viscomi. This book presents 14 specific rules that will cut 25% to 50% off response time when users request a page.

Performance 75

Performance Social Media Website Website Performance 75

Highly Scalable

MAY 1, 2012

Let us start with a simple example that illustrates capabilities of probabilistic data structures: Let us have a data set that is simply a heap of ten million random integer values and we know that it contains not more than one million distinct values (there are many duplicates). what is the cardinality of the data set)?

Analytics 191

Analytics Traffic Big Data Efficiency 191

O'Reilly

SEPTEMBER 19, 2023

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.

Hardware 99

Hardware Storage Big Data Blockchain 99

The Netflix TechBlog

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. 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.

Storage 203

Storage Latency Efficiency Data Engineering 203