Remove Data Warehouse Remove Manufacturing Remove Measurement Remove Reporting
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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. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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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.

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

Cloudera

At these times, they run business growth reports, shareholder reports, and financial reports for their earnings calls, to name a few examples. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.

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

Laminar Security

This is further exacerbated by the employment of outdated processes or solutions that are ill-equipped to cater to the demands of present-day cloud data security. Traditional methods of data management are no longer sufficient for handling the vast and complex data landscape.

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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?”