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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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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. This will be your OLTP data store for transactional data. version cluster. version cluster.

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Data Modeling 101: OLTP data modeling, design, and normalization for the cloud

erwin

I was pricing a data warehousing project with just 4 TB of data – small by today’s standards. I chose “OnDemand” for up to 64 virtual CPUs and 448 GB of memory, since this data warehouse wanted to leverage in-memory processing. So that’s $136,000 per year just to run this one data warehouse in the cloud.

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The Key to Faster Impact Analysis: Automated Data Lineage

Octopai

To make changes to a system, report, or process, BI developers must first perform impact analysis in order to gauge the potential impact of making such a change on the rest of the environment. With this problem solved, the Department of Transportation sent a memo to insurance companies informing them of the impending change and moved along.

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

AWS Big Data

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

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Business Intelligence vs Data Science vs Data Analytics

FineReport

Definition: BI vs Data Science vs Data Analytics. Business Intelligence describes the process of using modern data warehouse technology, data analysis and processing technology, data mining, and data display technology for visualizing, analyzing data, and delivering insightful information.

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Benefits of Enterprise Modeling and Data Intelligence Solutions

erwin

Data Modeling with erwin Data Modeler. a technology manager , uses erwin Data Modeler (erwin DM) at a pharma/biotech company with more than 10,000 employees for their enterprise data warehouse. Once everything is reviewed, then we go on to discuss the physical data model.”. “We George H., For Rick D.,