Scalegrid

FEBRUARY 8, 2024

By breaking up large datasets into more manageable pieces, each segment can be assigned to various network nodes for storage and management purposes. 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.

Storage 130

Storage Systems Big Data Azure 130

Scalegrid

DECEMBER 21, 2023

This includes latency, which is a major determinant in evaluating the reliability and performance of your Redis® instance, CPU usage to assess how much time it spends on tasks, operations such as reading/writing data from disk or network I/O, and memory utilization (also known as memory metrics).

Strategy 130

Strategy Monitoring Latency DevOps 130

ACM Sigarch

DECEMBER 6, 2018

As a result of these different types of usages, a number of interesting research challenges have emerged in the domain of visual computing and artificial intelligence (AI). Last but not least, the ability to auto-generate optimal neural networks (e.g. interactive AR/VR, gaming and critical decision making).

Wireless 54

Wireless IoT Analytics Architecture 54

Abhishek Tiwari

DECEMBER 13, 2017

Jeff is a Google Senior Fellow in the Google Brain team and widely known as a pioneer in artificial intelligence (AI) and deep learning community. Apart from indexes, super efficient sorting and join operations are some major areas come to my mind with immediate benefits of using learned data structure.

Artificial Intelligence 40

Artificial Intelligence Hardware Google Benchmarking 40

Abhishek Tiwari

DECEMBER 27, 2017

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. In contrast, Alluxio a middleware for data access - think Alluxio storage layer as fast cache. Machine learning meets data integration.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49