• Product Architecture

    Product Architecture

    StreamScape Real-Time Data Fabric combines stream processing, data virtualization and in-memory computing into a unified platform for Real-Time Business Intelligence.

    Engineered for performance and ease-of-use StreamScape lets organizations finally bridge the gap between business expectations and the challenges of Big + Fast Data.
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Built for Real-Time Integration + Analysis

Digital transformation relies on connecting data with the systems and people that use it. But modern data systems are a complex mix of cloud apps, in-flight data, file storage and traditional databases.

To interconnect everything often requires an assembly line of tools and specialists, making digital transformation a long and expensive process. StreamScape's Data Fabric provides a single architecture for working with any type of data. Batch or real-time, big data or small, engineers and architects can use the platform to accelerate insight delivery and automate decision making.

Make use of Reactive Programming for data agility and performance without the need for specialized languages or cumbersome frameworks. Triggers, Actors and Event Collections make it easy to build data pipelines and work with streaming data using familiar SQL syntax, eliminating the complexities of distributed system development.

A robust Data Virtualization layer lets you access external systems, files, relational databases, No-SQL and Big Data storage; allowing them to be queried and joined to any other data. Virtual collections let you integrate data from any source and easily load it into memory for fast processing and aggregation.

In-Memory Computing with SQL Support

StreamScape includes an in-memory data grid technology called Dataspaces™ for advanced SQL query
and streaming analytics. Combining in-memory data with parallel processing lets the data fabric process and analyze structured or unstructured data 100-1,000 times faster than other solutions.

Flexible persistence options let you define which data is maintained in memory for efficient trade off between infrastructure cost and performance. Multi-model schema support allows users to manage and analyze massive amounts of data as tables, documents, key-value maps, queues or columnar store. Natural data compression and multi-dimensional arrays make it ideal for Data Science and Probabalistic Analysis computations.

Small Footprint and Infinite Scale-Out

The StreamScape Data Engine implements a distributed 'shared nothing' architecture with co-located storage for ultra-fast performance. Small footprint, containerized design includes security and management features making deployment and scale-out a snap. Create modern, real-time applications for digital transformation, marketing, or regulatory compliance initiatives that leverage cloud, web, mobile, Internet of Things and Big Data storage.

A rich Semantic Data layer supports data modeling, ontology and schema definition for representing real-world things. Data validation and shaping rules let you easily change models to keep up with business needs.

Cut through the clutter and noise of data ingest by easily mapping files and unstructured data. Query your Data Lake, AWS S3, Big Data storage like Hadoop or MongoDB using SQL syntax for exploration and analysis of structured or unstructured data.

Streaming Analytics + Traditional BI

Only StreamScape lets you work with streaming data, relational datasets, NoSQL systems and data pipelines using one, common SQL-based query language. Join messages, data streams, tables and documents from multiple sources using ad-hoc queries. Aggregate data using familiar SQL functions or perform computations across sliding time windows; allowing you to process large volumes of data efficiently using a relatively small hardware footprint.

Advanced stream processing features let you use actors, triggers, event and snapshot tables for real-time probabalistic analysis of streaming data. Seamlessly blend streaming data with relational tables, files or unstructured data for easy acces by visualization tools like Tableau or Power BI as well as cloud and mobile applications.

Change Data Capture + Replication

StreamScape’s change capture and data replication combines virtualization, stream processing and rich data transformation capabilities, making real-time integration of SQL, NoSQL sources, cloud applications and messaging systems easy for non-technical staff to setup and maintain.

Specialized triggers capture changes into Journal File Tables that are external to the database, offloading processing to the data fabric and minimizing impact to the source system. Unlike log-based technologies, triggers support a broad variety of sources including non-transactional systems and object databases.

Simplify massive data ingestion into Data Lakes and Big Data platforms. Filter, transform, aggregate and enrich data in-flight while maintaining transactional integrity with automatic data validation and de-duplication. Built-in monitoring tracks key metrics such as volume and lag time to verify source and target data consistency.

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Real-Time Data Fabric™ and get started!


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