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F1 RDBMS by Google

Abstract by Google

google

Abstract: Many of the services that are critical to Google’s ad business have historically been backed by MySQL. We have recently migrated several of these services to F1, a new RDBMS developed at Google. F1 implements rich relational database features, including a strictly enforced schema, a powerful parallel SQL query engine, general transactions, change tracking and notification, and indexing, and is built on top of a highly distributed storage system that scales on standard hardware in Google data centers. The store is dynamically sharded, supports transactionally-consistent replication across data centers, and is able to handle data center outages without data loss.

Magic Quadrant for DW DBMS

Source of Magic Quadrant for Data Warehouse Database Management Systems, 28 January 2011

magic-quadrant-for-dw

Thoughts about “Enterprise Data Modeling: 7 Mistakes You Can’t Afford to Make”

My notes about interesting article “Enterprise Data Modeling: 7 Mistakes You Can’t Afford to Make“

mistake

About “Mistake 1: Forgetting that an enterprise architecture is a living framework”
An enterprise architecture is a living framework but it is not 100%-true.
“Living framework” in the projection to complement and minimal changes in the main part of schema (model).
If you have an application using the schema (er-schema) will be very expensive (time, cost) to change the application.
Talk about the life of the model (logical) is not correct in my view, correct to speak of the complement – the development of the model.
In my opinion development of the schema (in particular logical) is very important and requires the most expensive in the design phase, as further changes are overhead.
I think we should not talk about “an enterprise architecture”, we need to talk about data models and management of these models within the enterprise.

Read more: Thoughts about “Enterprise Data Modeling: 7 Mistakes You Can’t Afford to Make”

BIg Data Now

book big data now


This collection represents the full spectrum of data-related content we’ve published on O’Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with “What is data science?” and from there we’ve pursued the various threads and themes that naturally emerged. Now, roughly a year later, we can look back over all we’ve covered and identify a number of core data areas:

Big Data Now: Current Perspectives from O’Reilly Radar
It’s free for downloading

NON EMPTY Filtering on PerformancePoint Scorecard

For those who are familiar with PerformancePoint scorecards and dashboards, you have probably run into a problem where empty dimension values cannot be dynamically filtered on scorecards if you map the dimension to the rows or columns on the scorecard in the dashboard designer. One simple way around this is to use the NONEMPTY MDX function along with an EXISTS function call.

what-is-olap

Consider the following example:

Read more: NON EMPTY Filtering on PerformancePoint Scorecard

How to backup only one cube in SSAS database

If you want to backup only SSAS Cube (not whole database) you can script cube structure using SSMS (SQL Server Management Studio) 

Choose Create To.. or Alter To...

ssas script cube

Read more: How to backup only one cube in SSAS database