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. In many cases join is performed on a finite time window or other type of buffer e.g. LFU cache that contains most frequent tuples in the stream. Towards Unified Big Data Processing.

Big Data 154

Big Data Processing Lambda Database 154

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. These distributed storage services also play a pivotal role in big data and analytics operations.

Storage 130

Storage Systems Big Data Azure 130

Abhishek Tiwari

DECEMBER 27, 2017

In 2018, we will see new data integration patterns those rely either on a shared high-performance distributed storage interface ( Alluxio ) or a common data format ( Apache Arrow ) sitting between compute and storage. For instance, Alluxio, originally known as Tachyon, can potentially use Arrow as its in-memory data structure.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

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. What’s their secret???

Blockchain 46

Blockchain Hardware Google Analytics 46