Remove Data Lake Remove Manufacturing Remove Structured Data
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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data. In practice, OTFs are used in a broad range of analytical workloads, from business intelligence to machine learning.

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Generative AI: 5 enterprise predictions for AI and security — for 2023, 2024, and beyond

CIO Business Intelligence

Enterprise use of AI tools will only grow, with industries like manufacturing leading the charge Our research shows that mirroring the broader AI trend, enterprises across industry verticals sharply increased their use of AI from May 2023 to June 2023, with sustained growth through August 2023.

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2020 Data Impact Award Winner Spotlight: Merck KGaA

Cloudera

Without meeting GxP compliance, the Merck KGaA team could not run the enterprise data lake needed to store, curate, or process the data required to inform business decisions. It established a data governance framework within its enterprise data lake. Driving innovation with secure and governed data .

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

My vision is that I can give the keys to my businesses to manage their data and run their data on their own, as opposed to the Data & Tech team being at the center and helping them out,” says Iyengar, director of Data & Tech at Straumann Group North America.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

You can find similar use cases in other industries such as retail, car manufacturing, energy, and the financial industry. In this post, we discuss why data streaming is a crucial component of generative AI applications due to its real-time nature.

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Quantitative and Qualitative Data: A Vital Combination

Sisense

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?”

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.