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Achieving Trusted AI in Manufacturing

Cloudera

According to Gartner , 80 percent of manufacturing CEOs are increasing investments in digital technologies—led by artificial intelligence (AI), Internet of Things (IoT), data, and analytics. Manufacturers now have unprecedented capacity to collect, utilize, and manage massive amounts of data. Eliminate data silos.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

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Munich Re Launches Enterprise-Wide Data-Driven Platform for Analytics

Alation

A lot of people in our audience are looking at implementing data lakes or are in the middle of big data lake initiatives. I know in February of 2017 Munich Re launched their own innovative platform as a cornerstone for analytics that involved a big data lake and a data catalog. Department of Commerce.

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Data for All: Empowering Users With AI, ML, and Analytics

Sisense

Soon after, we announced the release of Sisense Hunch which provides the ability to transform even the most massive data sets into a deep neural net which can be placed anywhere, even on an IoT device. The hardware is there, the data is accumulating fast. Our investment in AI is paying off in spades.

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Migrate from Amazon Kinesis Data Analytics for SQL Applications to Amazon Kinesis Data Analytics Studio

AWS Big Data

In our solution, we create a notebook to access automotive sensor data, enrich the data, and send the enriched output from the Kinesis Data Analytics Studio notebook to an Amazon Kinesis Data Firehose delivery stream for delivery to an Amazon Simple Storage Service (Amazon S3) data lake. Choose Next.

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Data platform trinity: Competitive or complementary?

IBM Big Data Hub

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data discoverability. Yet another decade passed.

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How Cargotec uses metadata replication to enable cross-account data sharing

AWS Big Data

Cargotec captures terabytes of IoT telemetry data from their machinery operated by numerous customers across the globe. This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization.