We are entering a new age for Enterprise Architecture that will clearly define competitive advantage for those that choose to adopt it. The discussion is being held on every front, and with many variations. The crux of the issue is the inexorable force pushing the enterprise forward to catch up with the surge of digital devices, cloud opportunities, and the constant barrage and demands for next generation Business Intelligence. How can your corporate IT maintain its solid, secure, and stable infrastructure at the same time it is becoming more agile so it can deliver new solutions and data access for all the constituents?
Now that Data Virtualization has come of age, there finally is a category of technologies that can truly trim down the overhead and dramatically reduce the time to market for most data access and delivery requirements. While one camp hunkers down to protect its ancient catacombs, another is soaring, delivering buried data, Big Data, cloud data, social media, streaming data and all manner of information live and aligned from multiple data sources to applications, Business Analytics, portals, and plenty of other data consumers.
One of the manifestations of Data Virtualization as its core technology is the Agile Data Warehouse, often called the Logical Data Warehouse (LDW). The classic Data Warehouse (DW) is periodically populated with enterprise data of all kinds for use throughout the business. It has served well, and will continue to bring some value, but as such, certainly no one could claim that itâ€™s agile. It simply cannot meet the current demands of this new generation of data accessibility.
The Data Warehouse has been around some thirty years as essentially a repository for storing corporate data. The information is brought together from various sources and stored in ways that could produce meaningful related data as needed for transactions, feeding miscellaneous applications, and also for reporting. DW projects consume a significant segment of the IT budget. The effort to define, design, and implement new data sets in a data warehouse results in backlogs that are prohibitive to support the fast pace of todayâ€™s data needs. Most companies will continue to use their DW as they move to the more agile approach, but they will rely on it mostly as a historical data repository for reporting and analytics.
What is a logical data warehouse
The idea of a â€œLogicalâ€ Data Warehouse is to create a model representative of all of the data required for a particular domain, application, or analytics exercise. The model can be queried just as one would query a database and the data is accessed from each source, live, with even complex SQL queries resolved. This capability is called Data Virtualization (â€œDVâ€). Data Virtualization tools, such as Enterprise EnablerÂ®, provide UIs for discovery of data from the sources, creating relationships across them, and packaging and hosting these instructions in query-able forms such as ODBC, JDBC, REST, OData, SharePoint BCS, and others. The more agile of the Data Virtualization tools make it unnecessary to define a huge model up front, as changes are easy and fast, leveraging the agile, iterative approach to delivering solutions.
As with many of the emerging patterns delivered by Agile Integration Software, data virtualization is a critical enabling technology for the Logical Data Warehouse. In fact, according to the Gartner Hype Cycle for Information Infrastructure, 2012, â€œthe Logical Data Warehouse (LDW) is a new data management architecture for analytics which combines the strengths of traditional repository warehouses with alternative data management and access strategy. The LDW will form a new best practices by the end of 2017.â€
Todayâ€™s Business Analytics cannot wait for a DW or its extension to be designed, programmed populated, Q/Aâ€™d, and approved for use. We need to combine Hadoop data, social media, real-time operations, and more. A new paradigm is the only solution, and the Logical Data Warehouse is part of that approach.