StreamSQL

Real-time applications require performance of sophisticated filtering and analytic operations on streams of input data, often integrated with stored data. Languages such as C++ or Java have historically been used as the standard development and programming tools for such applications; however, relying on low-level programming schemes often means long, development cycles that consume skilled programming resources. Many organizations need greater business agility than is possible with custom-coding, and today there's a better approach.

StreamBase's programming model, using StreamSQL, addresses these requirements by providing ease of use, flexibility, and extensibility for developers in the following ways:

1. A Familiar, Standard Paradigm: The optimal approach for any event processing platform is to leverage a high-level language, using familiar, well-proven relational operators adapted for use in event processing. SQL's combination of functionality, power, and relative ease-of-use has made it an enduring standard for complex data transformations. StreamBase's data management experts extended the standard SQL querying model and operators to also perform processing on continuous data streams — and developed StreamSQL.  Developers building applications with StreamSQL can use the graphical EventFlow paradigm or a standards-based text form of the language.
2. Querying over Time Windows: StreamSQL extends the semantics of standard SQL (which assumes records in a finite stored dataset) by adding rich windowing constructs and stream-specific operators. With StreamSQL, the window construct defines the "scope" of a multi-message operator such as an aggregate or a join, letting it know when to finish an operation and output an answer. Windows are definable over time, number of messages, or breakpoints in other message attributes.
3. Powerful Operators: StreamSQL operators provide the capability to filter streams, merge, combine, and correlate multiple streams, and run time-window-based aggregations and computations on real-time streams or stored tables. The operators manage stream disorder and late or missing data, perform pattern-matching functions, and also access and manipulate in-memory and external storage. A full list of StreamSQL operators is found in our "StreamBase: Real-Time, Low Latency Data Processing"  whitepaper downloadable in the Knowledge Center.
4. Customization & Extensibility: Because the StreamSQL operator set is highly extensible, developers can easily achieve new processing functionality within the system, such as implementing a proprietary analysis algorithm, or creating user-defined aggregates, functions, and custom operators. And for developers that want to add other specialized capabilities, StreamBase also readily incorporates existing C++ or Java code and provides standard C++ and Java application programming interfaces.

StreamSQL is so easy to learn that many customers and partners build their first application via our graphical development environment in just a few hours, and have working prototypes up and running in days. If you are looking for a faster, more efficient approach to solving your next real-time problem, try StreamBase today.