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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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Seven Steps to Success for Predictive Analytics in Financial Services

Birst BI

An analytics alternative that goes beyond descriptive analytics is called “Predictive Analytics.”. Predictive Analytics: Predicting Future Outcomes. While descriptive analytics are focused on historical performance, predictive analytics are about predicting future outcomes.

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Don’t Blink: You’ll Miss Something Amazing!

Cloudera

Different data streams will have different characteristics, and having a platform flexible enough to adapt, with things like flexible partitioning for example, will be essential in adapting to different source volume characteristics. Kudu has this covered. The post Don’t Blink: You’ll Miss Something Amazing!

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Introduction To The Basic Business Intelligence Concepts

datapine

Business intelligence concepts refer to the usage of digital computing technologies in the form of data warehouses, analytics and visualization with the aim of identifying and analyzing essential business-based data to generate new, actionable corporate insights. The data warehouse. 1) The raw data.

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Enhance your security posture by storing Amazon Redshift admin credentials without human intervention using AWS Secrets Manager integration

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. On the provisioned snapshot dashboard, on the Restore snapshot menu, choose Restore to provisioned cluster or Restore to serverless namespace.

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What are decision support systems? Sifting data for better business decisions

CIO Business Intelligence

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

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Digital Transformation is a Data Journey From Edge to Insight

Cloudera

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.