The interest in dynamic data warehousing (DDW) is growing significantly. A sure sign that the virtual brand has reached take-off speed is claims by bloggers that they did not get enough credit for coining the concept, viral brand or idea. For example, Dan Linstedt recently went on what he described as a "rant" about "dynamic data warehousing."
Check out our Top 10 tips on Analysis Services 2005 that will help you navigate through the features and functionality of this priceless Business Intelligence tool.
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In this article, we will examine some rarely explored areas that concern foreign keys; in particular, we will look at disabled and un-trusted foreign keys.
All of us would like to code faster and with fewer mistakes. While a third party tool usualy won't help with the latter, there are a few to help you increase the speed at which you build readable code. New author Tom Fisher brings us a short tutorial on using a couple of Red Gate's tools.
In this series, Brian Knight shows you an actual difficult business problem to solve, gives you hints about how to solve it and then lastly shows you step-by-step instructions how to solve it if you want the help. n this video of the series, Brian demonstrate how to unpivot data that may arrive from the mainframe and load a many to many table with SSIS.
Steve Jones takes a look at energy news and how Google and Microsoft are looking to lower their dependence on the grid and advance alternative energy use.
This article contains information about the things we have learned while working with Federated Databases. Before beginning it is necessary to define the terms being used. Included in this article is one solution in production that is using federated servers and Distributed Partitioned Views.
This white paper describes how to set up a load-balanced scalable querying environment for Microsoft SQL Server 2005 Analysis Services so that you can handle a large number of concurrent queries to your Analysis Services servers. Load-balanced querying ensures that readers of OLAP cubes can consistently query for the latest aggregations throughout the day and distribute the load of all queries among the available servers. This scale-out querying architecture optimizes cube processing time, increases the frequency of cube update, and makes processing more robust as you can afford more frequent processing and transparent error recovery.
Every organization I talk to has the same problem dressed up in different clothes....
By DataOnWheels
I am delighted to host this month’s T-SQL Tuesday invitation. If you are new...
By alevyinroc
Ten years (and a couple jobs) ago, I wrote about naming default constraints to...
Comments posted to this topic are about the item The day-to-day pressures of a...
Comments posted to this topic are about the item Data Modeling with dbt for...
Comments posted to this topic are about the item Data Modeling with dbt for...
I have some data in a table that looks like this:
BeerID BeerName brewer beerdescription 1 Becks Interbrew Beck's is a German-style pilsner beer 2 Fat Tire New Belgium Toasty malt, gentle sweetness, flash of fresh hop bitterness. 3 Mac n Jacks Mac & Jack's Brewery This beer erupts with a floral, hoppy taste 4 Alaskan Amber Alaskan Brewing Alaskan Brewing Amber Ale is an "alt" style beer 8 Kirin Kirin Brewing Kirin Ichiban is a Lager-type beerIf I run this, what is returned?
select t1.key
from openjson((select t.* FROM Beer AS t for json path)) t1 See possible answers