Smart Data Collective

MARCH 27, 2019

. Frugal living has become a major fad since the onset of the recession in 2008. Gaurav Deshpande of the Big Data and Analytics Hub from IBM highlighted this. Capturing and using location data requires tools that are capable of handling large volumes of data at high velocity. Consumers saved $3.1 Merging deals.

Hadoop 84

Hadoop Big Data Big Data Database 84

phData

NOVEMBER 8, 2024

For instance, partition pruning, data skipping, and columnar storage formats (like Parquet and ORC) allow efficient data retrieval, reducing scan times and query costs. This is invaluable in big data environments, where unnecessary scans can significantly drain resources.

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

Women in Big Data

FEBRUARY 27, 2023

We are delighted and honored to showcase the inspiring career journey of Theresa Kushne , a member of Women in Big Data’s board. During my time at IBM, I served as the Director of EMEA Direct Marketing and discovered something very important – we didn’t have data for the new world of email marketing.

Big Data 52

Big Data Big Data Analytics Analytics 52

Dataconomy

MARCH 16, 2023

It is essentially a decentralized database that enables users to store and share information in a tamper-proof and immutable manner. The technology was initially introduced in 2008 as the underlying technology behind Bitcoin, the first cryptocurrency, and has since gained widespread adoption in various industries.

Algorithm 70

Algorithm Database Big Data Big Data 70

Alation

AUGUST 5, 2021

Artificial Intelligence (AI) and Machine Learning (ML) As more companies implement Artificial Intelligence and Machine Learning applications to their business intelligence strategies, data users may find it increasingly difficult to keep up with new surges of Big Data. Is the data true and factual?

Data Quality 52

Data Quality Data Governance Artificial Intelligence Artificial Intelligence 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. SIMT describes processors that are able to operate on data vectors and arrays (as opposed to just scalars), and therefore handle big data workloads efficiently.

AWS 94

AWS ML ML Clustering 94