White Paper: DataStax Enterprise

Title: DataStax Enterprise

Description: Successful businesses recognize the value of capturing the massive volume of daily customer interactions – from purchased transactions to what products customers looked at – and analyzing that data to discover insights about their customers that help make smart business decisions. Achieving success in this area requires two similar yet distinct types of technology: a real-time database infrastructure that supports the operational data needs of the business, and an analytical framework capable of handling the massively parallel analysis of that data.

The real-time side of modern data management has experienced a shift in direction over the past few years. Traditional RDBMS technologies have not been able to keep up with the explosive growth of new data types requiring millisecond-time performance access on a scale that involves both large numbers of concurrent users and high data volumes. Whether the application is serving high-volume web session and user data, reacting to a high-speed financial market feed, aggregating distributed sensor grid events, processing social network messages and connections, or providing real-time intelligence and entity classification, it all comes down to being able to process, store, and respond to large data volumes as fast as possible.

Once such real-time data is stored, it is only natural for decision makers to use it for analysis purposes. However, challenges such as mixed workload management (for instance, separating real-time and analytic operations on the same data), a distributed business and workforce, and the need to store and process extremely large sets of data have stymied even the best of IT professionals who try to use legacy RDBMS software to squeeze the proverbial square peg into the round hole.

This paper examines these and other key data management challenges facing modern businesses. It also explains how DataStax Enterprise provides the first post-relational database solution that handles both real-time and analytic data in a way that solves these problems without the major compromises and costs associated with using RDBMS solutions.