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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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Saving Patient Lives with a Tech Innovation That Cuts Medical Error

CIO Business Intelligence

You have to think about the day-to-day life of a clinician,” the COO of German software development company XANTAS AG observed, “and how laboratory values (measurements determining a patient’s health) can be overlooked. I also started at SAP and began working on data warehousing there. Power of knowledge.

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Cloudera Streaming Analytics 1.4: the unification of SQL batch and streaming

Cloudera

We believe this new capability will unlock net new capabilities for use cases in IoT, Finance, Manufacturing and more. This gives customers the ability to create unique ETL flows, real-time data warehousing, and create valuable feeds of data without massive infrastructure redesign. Reading and enriching with batch data.

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Data as a service: Top vendors offering data on tap

CIO Business Intelligence

DaaS vendors can also improve the quality of data that an organization might otherwise gather itself by correcting errors or filling in gaps and even provide big blocks of data should you need more. In this way, DaaS providers can improve your homegrown data warehouse by cross-fertilizing it with other, curated sources.

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Extreme data center pressure? Burst to the cloud with CDP!

Cloudera

Your sunk costs are minimal and if a workload or project you are supporting becomes irrelevant, you can quickly spin down your cloud data warehouses and not be “stuck” with unused infrastructure. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.

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Leveraging AI to discover and classify your data in a complex and dynamic landscape

Laminar Security

They offer a comprehensive solution to enhance your cloud security posture and effectively manage your data. The primary focus of discovery is to find all the places where data exists and identify the assets it resides in. Privacy concerns are also paramount when using AI for data discovery and classification.

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10 key roles for AI success

CIO Business Intelligence

You don’t understand how long you should test your feature and what exactly you should measure,” he says. Data scientists may build the ML models, but its ML engineers who implement them. AI strategists can also help organizations obtain the data they need to fuel AI effectively. For all of this, you have to apply AI methods.”.