Percona

JANUARY 19, 2023

Compression: Compression is the process of restructuring the data by changing its encoding in order to store it in fewer bytes. There are many compression tools and algorithms for data out there. It was developed for optimizing data storage and access for big data sets. 1 mysql mysql 592K Dec 30 02:48 tb1.ibd

Open Source 87

Open Source Storage Database Big Data 87

Scalegrid

FEBRUARY 8, 2024

Key Takeaways Distributed storage systems benefit organizations by enhancing data availability, fault tolerance, and system scalability, leading to cost savings from reduced hardware needs, energy consumption, and personnel. This process effectively duplicates essential parts of information to safeguard against potential loss.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

MAY 1, 2023

IT operations analytics is the process of unifying, storing, and contextually analyzing operational data to understand the health of applications, infrastructure, and environments and streamline everyday operations. ITOA automates repetitive cloud operations tasks and streamlines the flow of analytics into decision-making processes.

Analytics 188

Analytics Artificial Intelligence Big Data Open Source 188

O'Reilly

JANUARY 13, 2020

” Willing also offered a shout-out to the CircuitPython and Mu projects, asking, “Who doesn’t love hardware, blinking LEDs, sensors, and using Mu, a user-friendly editor that is fantastic for adults and kids?” ” Java. It’s mostly good news on the Java front. ” What lies ahead?

Programming 130

Programming Java Google C++ 130

All Things Distributed

DECEMBER 20, 2017

For example, Kärcher, the maker of cleaning technologies, manages its entire fleet through the cloud solution "Kärcher Fleet" This transmits data from the company's cleaning devices e.g. about the status of maintenance and loading, when the machines are used, and where the machines are located. More than mere support.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

All Things Distributed

SEPTEMBER 6, 2013

Shell leverages AWS for big data analytics to help achieve these goals. Due to the exponential growth of the biology and informatics fields, Unilever needs to maintain this new program within a highly-scalable environment that supports parallel computation and heavy data storage demands.

Cloud 129

Cloud Energy AWS Healthcare 129

All Things Distributed

JULY 13, 2010

Customers with complex computational workloads such as tightly coupled, parallel processes, or with applications that are very sensitive to network performance, can now achieve the same high compute and networking performance provided by custom-built infrastructure while benefiting from the elasticity, flexibility and cost advantages of Amazon EC2.

Cloud 118

Cloud AWS Automotive Latency 118

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

Dynatrace

JUNE 29, 2022

A hybrid cloud, however, combines public infrastructure and services with on-premises resources or a private data center to create a flexible, interconnected IT environment. Hybrid environments provide more options for storing and analyzing ever-growing volumes of big data and for deploying digital services.

Infrastructure 182

Infrastructure Cloud Azure AWS 182

All Things Distributed

OCTOBER 7, 2015

In such a data intensive environment, making key business decisions such as running marketing and sales campaigns, logistic planning, financial analysis and ad targeting require deriving insights from these data. However, the data infrastructure to collect, store and process data is geared toward developers (e.g.,

Cloud 137

Cloud Big Data AWS Analytics 137

Abhishek Tiwari

DECEMBER 27, 2017

A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

The Morning Paper

SEPTEMBER 19, 2019

It handles an order of magnitude more throughput than a prototype built on a stream processing engine. Could it be Analyzing efficient stream processing on modern hardware ? .” Crusher is a Google system for automatically discovering email templates (e.g. for machine generated emails sent to humans). What’s their secret???

Blockchain 46

Blockchain Hardware Google Analytics 46

All Things Distributed

NOVEMBER 15, 2010

From financial processing and traditional oil & gas exploration HPC applications to integrating complex 3D graphics into online and mobile applications, the applications of GPU processing appear to be limitless. Because of its focus on latency, the generic CPU yielded rather inefficient system for graphics processing.

AWS 136

AWS Latency Programming Architecture 136

ACM Sigarch

NOVEMBER 1, 2019

This blog post gives a glimpse of the computer systems research papers presented at the USENIX Annual Technical Conference (ATC) 2019, with an emphasis on systems that use new hardware architectures. Intel Quick Assist Technology (QAT) was the focus of the QZFS paper which used this new hardware device to speed up file system compression.

Architecture 12

Architecture Hardware Cache Storage 12

All Things Distributed

NOVEMBER 15, 2016

Previously, I wrote about Amazon QuickSight , a new service targeted at business users that aims to simplify the process of deriving insights from a wide variety of data sources quickly, easily, and at a low cost. Put simply, data is not always readily available and accessible to organizational end users.

Analytics 152

Analytics Availability Media Social Media 152

All Things Distributed

NOVEMBER 30, 2016

There are many more application areas where we use ML extensively: search, autonomous drones, robotics in fulfillment centers, text processing and speech recognition (such as in Alexa) etc. And this process must be repeated for every object, face, voice, and language feature in an application.

AWS 165

AWS Lambda Artificial Intelligence Mobile 165

ACM Sigarch

APRIL 8, 2019

big-data processing, machine learning, quantum computing, and so on). This is arguably a fundamentally hard problem for computer architecture, but efforts towards open source hardware (eg. Her current work focuses on hardware/software co-design for extremely large-scale deep learning training. Lack of Diversity.

Architecture 74

Architecture Open Source Hardware Software Engineering 74