Trends in Data Warehousing: A Practitioner’s View

Industries experience with data warehousing over the last decade has provided important lessons on what works in today’s business intelligence (BI) solutions. It is not only these lessons, but also the emerging trends which are also shaping our industry directions in business solutions. As a result, our emerging reference architectures used in building these enterprise data warehouse solutions are changing to meet business demands.
This evolving reference architecture used in building solutions will be overviewed, followed by the implications of these changes. It is these evolving reference architectures that are putting new demands on the databases that are used in warehousing. An important point is that although many of these concepts are not new, databases are being pushed in new ways which are requiring further technology invention.
With the emergence and evolution of the intranet, as well as more businesses exploiting semi-structured data, the more traditional business models are evolving with respect to such things as data accessibility, delivery, and concurrency. Technology such as XML and webservices become more critical as databases integrate with web portals and BI tooling. Moreover, additional demands on more broad decision making within enterprises are causing heavy consolidation and non-traditional mixed workloads (heavily mixing OLTP and DSS) beyond what has been conventional in the past. Service level agreements, as well as normal operational characteristics are not the same (e.g., backups). Moreover, in many cases the consolidation is not an option and or desired. In such latter cases, the business question still needs to be run. As a result, federation augmentation is also very real in enterprise systems. Query management in a federated environment is still a challenging task. A combination of consolidation and federation augmentation is being seen.
In addition to heavy consolidation and federation augmentation, both real-time (right-time) and active data warehousing systems are being built. These systems present interesting challenges to traditional maintenance and extract/transformation/load operational procedures. Specifically, in large multi-terabyte systems which are 24x7x365. Queries in such systems that execute over aggregated data (including materialized views) need to be very close in time to a consolidated operational data store (ODS) in the same enterprise data warehouse. The maintenance challenges are pushing the technology. Finally, the closed loop processing in an enterprise-wide solution, allows warehouses to play an even more crucial role. Not only are operational systems creating events, so are data warehouses; they play a crucial active role in an enterprise. One such example of events produced in a warehouse is measures, which may be key business indicators (KPIs) used in business performance monitoring through portals.
In addition to this talk presenting emerging data warehousing reference architectures, trends and directions shaping these enterprise data warehousing installations will be overviewed. In doing so, some key implications to databases will be highlighted. In addition to the database itself, any warehouse solution consists of a solution stack. Implications on the whole stack will be touched upon, including such things as metadata and interoperability via standard interfaces such as XML.

Data Warehousing Trends

Friends , today Very few enterprises set out to there remedy data quality problems just for the sake of data quality. So what’s pushing enterprises to actually do something about data quality, instead of just talking about it? First, poor data quality costs them money in terms of lost productivity, faulty business decisions and an inability to achieve results from expensive investments in enterprise applications. Second, poor data quality can make regulatory compliance extremely difficult. It’s true that many companies have cleaned up their customer data to enable CRM-related initiatives. However, their focus has now turned to data in other areas of the business, such as supply chain and finance, and to tackling what can seem like intractable data quality problems in nearly every business domain.

Galaxy Schema

Galaxy Schema is combination of more than one star schema.