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

erwin

Designing databases for data warehouses or data marts is intrinsically much different than designing for traditional OLTP systems. Accordingly, data modelers must embrace some new tricks when designing data warehouses and data marts. Figure 1: Pricing for a 4 TB data warehouse in AWS.

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Data Governance and Metadata Management: You Can’t Have One Without the Other

erwin

Therefore, most enterprises have encountered difficulty trying to master data governance and metadata management, but they need a solid data infrastructure on which to build their applications and initiatives. Data Governance Attitudes Are Shifting. Metadata Management Takes Time.

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Financial Intelligence vs. Business Intelligence: What’s the Difference?

Jet Global

A new paradigm in reporting and analysis is emerging. There was always a delay between the events being recorded in financial systems (for example, the purchase of a product or service) and the ability to put that information in context and draw useful conclusions from it (for example, a weekly sales report).

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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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Data Modeling 401 for the cloud: Database design for serverless data-bases in the cloud

erwin

I want to wrap up this blog by giving you a very specific example of how a simple data modeling mistake can increase resource consumption and thus expenses of serverless databases. Imagine that you have a rather large data model with lots of entities and attributes. Watch the webinars : erwin Data Modeling 101-401 for the cloud.

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Inside the Mind and Methodology of a Data Scientist

Birst BI

To hear more on Infor Dynamic Science Labs analytic methodologies, watch this on-demand webinar. After a few iterations, this results in a well-defined business question with identifiable supporting data. A foundational data analysis tool is Statistics , and everyone intuitively applies it daily.

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Seven Common Challenges Fueling Data Warehouse Modernisation

Cloudera

Enterprise data warehouse platform owners face a number of common challenges. In this article, we look at seven challenges, explore the impacts to platform and business owners and highlight how a modern data warehouse can address them. ETL jobs and staging of data often often require large amounts of resources.