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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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The Award Winning Formula: How Cloudera Empowered OCBC With Trusted Data To Unlock Business Value from AI

Cloudera

Recently, Cloudera, alongside OCBC, were named winners in the“ Best Big Data and Analytics Infrastructure Implementation ” category at The Asian Banker’s Financial Technology Innovation Awards 2024.

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Achieving Trusted AI in Manufacturing

Cloudera

Here are some of the key use cases: Predictive maintenance: With time series data (sensor data) coming from the equipment, historical maintenance logs, and other contextual data, you can predict how the equipment will behave and when the equipment or a component will fail. Eliminate data silos.

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

Cloudera

After countless open-source innovations ushered in the Big Data era, including the first commercial distribution of HDFS (Apache Hadoop Distributed File System), commonly referred to as Hadoop, the two companies joined forces, giving birth to an entire ecosystem of technology and tech companies. That’s today’s Cloudera.

Big Data 107
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What Makes Data-in-Motion Architectures a Must-Have for the Modern Enterprise

Cloudera

What matters is how fast that data can be captured, processed to put that vibration reading within the context of the machine’s health, used to identify an anomaly, and delivered to a person or system that can take action. A traditional analytics stack typically has this functionality spread out over multiple inefficient steps.