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Data Modeling 301 for the cloud: data lake and NoSQL data modeling and design

erwin

For NoSQL, data lakes, and data lake houses—data modeling of both structured and unstructured data is somewhat novel and thorny. As with the part 1 and part 2 of this data modeling blog series, the cloud is not nirvana. Data modeling basics. Data Modeling. This blog is based upon a recent webcast that can be viewed here.

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Data Modeling 201 for the cloud: designing databases for data warehouses

erwin

Accordingly, data modelers must embrace some new tricks when designing data warehouses and data marts. Data modeling for the cloud: good database design means “right size” and savings. Now to cover some data modeling basics that applies no matter whether on-premises or in the cloud. Data Modeling. Business Focus.

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Time to Act: Key Considerations When Leaving the Legacy Behind

Jedox

Users should be able to create their own integrated plans and reports based on the same data model and business logic. Legacy solution users will also want to look for familiar features: an in-memory OLAP database, an easy to model multi-cube architecture and cell-based spreadsheet style reporting.

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AI vs. BI for Business, What Do You Need?

Jet Global

AI, colloquially, is used to refer to a number of computer-powered business decision drivers, including automation (not AI), data modeling (not AI), and reporting and analytics (also not AI). But are those tools powered by artificial intelligence? What are some of the core components of business intelligence?

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What Is Embedded Analytics?

Jet Global

These licensing terms are critical: Perpetual license vs subscription: Subscription is a pay-as-you-go model that provides flexibility as you evaluate a vendor. Pricing model: The pricing scale is dependent on several factors. It is organized to create a top-down model that is used for analysis and reporting.

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The Missing Link in Your Power BI/ERP Data Chain

Jet Global

If you use Power BI alone to generate reports, the complexity of the Microsoft Dynamics data model can be an obstacle as it requires knowledge of its proprietary DAX scripting language. Data models must be refreshed either manually or on a set schedule. Providing pre-built OLAP cubes, a data warehouse, and visualized dashboards.