These step-by-step guidelines will help dimension managers and users drill across disparate databases.
"Integration" is one of the older terms in data warehousing. Of course, almost all of us have a vague idea that integration means making disparate databases function together in a useful way. But as a topic, integration has taken on the same fuzzy aura as "meta data." We all know we need it; we don't have a clear idea of how to break it down into manageable pieces; and above all, we feel guilty because it is always on our list of responsibilities. Does integration mean that all parties across large organizations agree on every data element or only on some data elements?
This article decomposes the integration problem into actionable pieces, each with specific tasks. We'll create a centralized administration for all tasks, and we'll "publish" our integrated results out to a wide range of "consumers." These procedures are almost completely independent of whether you run a highly centralized shop on one physical machine, or whether you have dozens of data centers and hundreds of database servers. In all cases, the integration challenge is the same; you just have to decide how integrated you want to be.
Fundamentally, integration means reaching agreement on the meaning of data from the perspective of two or more databases. Using the specific notion of "agreement," as described in this article, the results of two databases can be combined into a single data warehouse analysis. Without such an accord, the databases will remain isolated stovepipes that can't be linked in an application.
It's very helpful to separate the integration challenge into two parts: reaching agreement on labels and reaching agreement on measures. This separation, of course, mirrors the dimensional view of the world. Labels are normally textual, or text-like, and are either targets of constraints or are used as "row-headers" in query results, where they force grouping and on-the-fly summarization. In a pure dimensional design, labels always appear in dimensions. Measures, on the other hand, are normally numeric, and, as their name implies, are the result of an active measurement of the world at a point in time. Measures always appear in fact tables in dimensional designs. The distinction between labels and measures is very important for our task of integration because the steps we must perform are quite different. Taken together, reaching agreement on labels and on measures defines integration.
The Agile ArchiveWhen it comes to managing data, donít look at backup and archiving systems as burdens and cost centers. A well-designed archive can enhance data protection and restores, ease search and e-discovery efforts, and save money by intelligently moving data from expensive primary storage systems.
2014 Analytics, BI, and Information Management SurveyITís tried for years to simplify data analytics and business intelligence efforts. Have visual analysis tools and Hadoop and NoSQL databases helped? Respondents to our 2014 InformationWeek Analytics, Business Intelligence, and Information Management Survey have a mixed outlook.