As of 2010 data virtualization had begun to advance ETL processing. The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. So-called Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured and unstructured data sources. ETL tools can leverage object-oriented modeling and work with entities’ representations persistently stored in a centrally located hub-and-spoke architecture. Such a collection that contains representations of the entities or objects gathered from the data sources for ETL processing is called a metadata repository and it can reside in memory or be made persistent. By using a persistent metadata repository, ETL tools can transition from one-time projects to persistent middleware, performing data harmonization and data profiling consistently and in near-real time.
A probabilistic database is an uncertain database in which the possible worlds have associated probabilities. Probabilistic database management systems are currently an active area of research. “While there are currently no commercial probabilistic database systems, several research prototypes exist…”
Probabilistic databases distinguish between the logical data model and the physical representation of the data much like relational databases do in the ANSI-SPARC Architecture. In probabilistic databases this is even more crucial since such databases have to represent very large numbers of possible worlds, often exponential in the size of one world (a classical database), succinctly.
Without a doubt, data analytics have a powerful new tool with the “map/reduce” development model, which has recently surged in popularity as open source solutions such as Hadoop have helped raise awareness.
Tool: You may be surprised to learn that the map/reduce pattern dates back to pioneering work in the 1980s which originally demonstrated the power of data parallel computing. Having proven its value to accelerate “time to insight,” map/reduce takes many forms and is now being offered in several competing frameworks.
If you are interested in adopting map/reduce within your organization, why not choose the easiest and best performing solution? ScaleOut StateServer’s in-memory data grid offers important advantages, such as industry-leading map/reduce performance and an extremely easy to use programming model that minimizes development time.
Here’s how ScaleOut map/reduce can give your data analysis the ideal map/reduce framework:
- ScaleOut StateServer’s in-memory data grids provide extremely fast data access for map/reduce. This avoids the overhead of staging data from disk and keeps the network from becoming a bottleneck.
- ScaleOut StateServer eliminates unnecessary data motion by load-balancing the distributed data grid and accessing data in place. This gives your map/reduce consistently fast data access.
- Automatic parallel speed-up takes full advantage of all servers, processors, and cores.
- Integrated, easy-to-use APIs enable on-demand analytics; there’s no need to wait for batch jobs.