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Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Sisense

Data warehouse vs. databases Traditional vs. Cloud Explained Cloud data warehouses in your data stack A data-driven future powered by the cloud. We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Data warehouse vs. databases.

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Simplify Online Analytical Processing (OLAP) queries in Amazon Redshift using new SQL constructs such as ROLLUP, CUBE, and GROUPING SETS

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale, massively parallel data warehouse that makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. This will allow for a smoother migration of OLAP workloads, with minimal rewrites.

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Database vs. Data Warehouse: What’s the Difference?

Jet Global

Whether the reporting is being done by an end user, a data science team, or an AI algorithm, the future of your business depends on your ability to use data to drive better quality for your customers at a lower cost. So, when it comes to collecting, storing, and analyzing data, what is the right choice for your enterprise?

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How Aura from Unity revolutionized their big data pipeline with Amazon Redshift Serverless

AWS Big Data

Aura from Unity (formerly known as ironSource) is the market standard for creating rich device experiences that engage and retain customers. Amazon Redshift is a recommended service for online analytical processing (OLAP) workloads such as cloud data warehouses, data marts, and other analytical data stores.

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Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

The company’s market power is based largely on its ability to promote the “stack”—that is, to position the entire suite of Microsoft products as a holistic solution to customer problems. For more powerful, multidimensional OLAP-style reporting, however, it falls short. OLAP reporting has traditionally relied on a data warehouse.

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Navigating Data Entities, BYOD, and Data Lakes in Microsoft Dynamics

Jet Global

For more sophisticated multidimensional reporting functions, however, a more advanced approach to staging data is required. The Data Warehouse Approach. Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible.

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The Business Intelligence Market – What’s Old is New

In(tegrate) the Clouds

As the data visualization, big data, Hadoop, Spark and self-service hype gives way to IoT, AI and Machine Learning, I dug up an old parody post on the business intelligence market circa 2007-2009 when cloud analytics was just a disruptive idea. Thanks to The OLAP Report for lots of great market materials.