Remarkably, almost 80% of the time dedicated to data analysis is actually spent on data preparation with less than 10% being spent on data modeling and looking for patterns. While data are ingested into analytical apps faster from an increasing number of sources, quality and freshness are often lagging.

Noisy, unreliable information affects business decisions and has direct impact on the bottom line. As such the conversation has pivoted from storage and management to enabling data agility. Agility is all about how fast you can extract value from mountains of data and how quickly information can be turned into action.

Big Data technologies like MongoDB are great for collecting large amounts of diverse information. But to ask any meaningful questions of the data you need to get it into a format that BI tools and analytics engines understand. This can be a slow and expensive process.

StreamScape Dataspaces™ simplify the task of data preparation, allowing users to access MongoDB documents using standard SQL. The query engine lets you work with MongoDB data in its native format by applying schema on-read. Results can be presented as tables or documents based on application needs.

This makes the data more nimble and minimizes the cost and effort of data preparation, allowing users to describe and present information to consumers in real-time as it is created, straight from the source.

The Reactive Data Platform™ provides powerful tools that put you in control of the infrastructure environment, making collaboration between Developers and Operations teams a seamless process.

Enterprise grade security keeps clients and application data safe. Multi-tenant application support protects MongoDB instances against co-mingling of data and API. Data privacy and policy-based rules integrate with external security and Delegate Authentication systems.

If you can describe it, you can query it. StreamScape’s extensible data modeling capabilities support rich semantics and schema definition for representing real-world things. On-the-fly validation and data shaping rules let you easily describe, and evolve your MongoDB schema, keeping up with the ever-changing business requirements.

A robust virtualization layer abstracts data from files, SQL databases, or big data storage (such as Hadoop), allowing it to be queried and joined to any other data or pinned into memory to speed up processing and aggregation.

The platform’s data integration features let users easily navigate between tables, documents, structured or unstructured data; making it easy to transform and package information, turning it into high-value product.

Deploy data services on MongoDB like a pro. StreamScape lets you scale up with analytics needs. Grow or shrink the data fabric’s capacity on-demand, based on resource usage.

Solve bigger problems faster. A modern, light-weight architecture for cloud and on-premise data processing, built to take advantage of commodity hardware makes it easy to manage and scale analytic applications.

Reactive programming lets applications process data asynchronously and makes working with data-in-motion an easy task. No need to learn new languages or complex syntax. With StreamScape the capabilities are built right into the SQL.

Seamlessly blend traditional analytics and streaming data processing, allowing information from MongoDB and other sources to be aggregated on-demand without the need for more data copies. Reduce data sprawl and eliminate the people and process bottleneck of traditional ETL-centric solutions.

If you know SQL and Java Script, you already know how to use the data engine. A powerful query engine lets you react to data changes and access information from disparate sources, simplifying data-driven decision making. Combine SQL and MongoDB syntax to speed up application development and deployment for unmatched data agility.

Easily access data collections using a mix of SQL and native Mongo syntax. Develop transactional functions and triggers on top of MongoDB.

Turn MongoDB data into a query-able data services accessible from browsers, .Net applications and Microsoft tools like Excel or Power BI.

Define a standards-based RESTful API for accessing MongoDB data from any language, Android or iPhone device.