Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. The engine should be compact and efficient, so one can deploy it in multiple datacenters on small clusters. High performance and mobility. Pipelining.

Big Data 154

Big Data Processing Lambda Database 154

ScaleOut Software

NOVEMBER 24, 2020

Rapid advances in the telematics industry have dramatically boosted the efficiency of vehicle fleets and have found wide ranging applications from long haul transport to usage-based insurance. Real-Time Digital Twins Can Add Important New Capabilities to Telematics Systems and Eliminate Scalability Bottlenecks.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Scalegrid

MARCH 28, 2024

Key Takeaways Redis offers complex data structures and additional features for versatile data handling, while Memcached excels in simplicity with a fast, multi-threaded architecture for basic caching needs. Introduction Caching serves a dual purpose in web development – speeding up client requests and reducing server load.

Cache 130

Cache Storage Scalability Architecture 130

Scalegrid

FEBRUARY 8, 2024

It utilizes methodologies like DStore, which takes advantage of underused hard drive space by using it for storing vast amounts of collected datasets while enabling efficient recovery processes. These systems enable vast amounts of data to be spread over multiple nodes, allowing for simultaneous access and boosting processing efficiency.

Storage 130

Storage Systems Big Data Azure 130

Dynatrace

OCTOBER 4, 2022

While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. What is a data lakehouse? Reduced redundancy.

Artificial Intelligence 208

Artificial Intelligence Storage Analytics Government 208

The Morning Paper

SEPTEMBER 19, 2019

Could it be Analyzing efficient stream processing on modern hardware ? Hyper Dimension Shuffle describes how Microsoft improved the cost of data shuffling, one of the most costly operations, in their petabyte-scale internal big data analytics platform, SCOPE. for machine generated emails sent to humans).

Blockchain 46

Blockchain Hardware Google Analytics 46

Abhishek Tiwari

DECEMBER 27, 2017

A unified data management (UDM) system combines the best of data warehouses, data lakes, and streaming without expensive and error-prone ETL. It offers reliability and performance of a data warehouse, real-time and low-latency characteristics of a streaming system, and scale and cost-efficiency of a data lake.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49