Remove Contextual Data Remove Data Analytics Remove Machine Learning
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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Addressing the Elephant in the Room – Welcome to Today’s Cloudera

Cloudera

This includes running analytics at the edge, supporting multi-cloud environments, treating Apache Iceberg as a first-class citizen, and introducing many more innovations like data observability. The Future of Enterprise AI, Delivered Today If the Big Data era was this century’s gold rush, then AI is the next moon shot.

Big Data 103
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Regeneron turns to IT to accelerate drug discovery

CIO Business Intelligence

Rigid requirements to ensure the accuracy of data and veracity of scientific formulas as well as machine learning algorithms and data tools are common in modern laboratories. When Bob McCowan was promoted to CIO at Regeneron Pharmaceuticals in 2018, he had previously run the data center infrastructure for the $81.5

Data Lake 124
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BRIDGEi2i Mentioned as Top 10 Data Science companies in India to work for 2020

bridgei2i

Analytics Insight is a publication focused on Artificial Intelligence, Big Data and Analytics. For this study, the publication evaluated companies that cover areas of Data Science – Data Analytics, Big Data, Business Analytics, Machine Learning, Artificial Intelligence & Deep Learning.

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AI recommendations for descriptions in Amazon DataZone for enhanced business data cataloging and discovery is now generally available

AWS Big Data

Introducing generative AI-powered data descriptions With AI-generated descriptions in Amazon DataZone, data consumers have these recommended descriptions to identify data tables and columns for analysis, which enhances data discoverability and cuts down on back-and-forth communications with data producers.

Metadata 101
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Building an Open Data Processing Pipeline for IoT

Cloudera

Last week Cloudera introduced an open end-to-end architecture for IoT and the different components needed to help satisfy today’s enterprise needs regarding operational technology (OT), information technology (IT), data analytics and machine learning (ML), along with modern and traditional application development, deployment, and integration.

IoT 40
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How to Build a Successful Metadata Management Framework

Alation

Establish business glossaries: Define business terms and create standard relationships for data governance. Collaborate more effectively: Break down data silos for better understanding of data assets across all business units. Supports Strong Data Culture. Simple Navigation for Business Users.