The Convergence of In-Memory Computing Technology
Everyone likes to think they are cool, but it is a totally different thing to be told you are cool by someone else, especially someone you hold in the highest regard. At Striim, we’ve built a great company with lots of brilliant people, and an amazing platform that is solving real world streaming integration and intelligence problems for our customers. And now, someone has told us that we are, officially, “cool.”
Striim has been selected as a “Cool Vendor” by Gartner, one of just a few companies chosen for the “Cool Vendors in In-Memory Computing Technology, 2016“ report. This report highlights vendors that have created new and innovative products and services in the broad category of In-Memory Computing.
The analysts at Gartner are the thought leaders in the space of In-Memory Computing, and break down the technologies involved into a number of different segments. These include In-Memory Databases (IMBDs), In-Memory Data Grids (IMDGs), High-Performance Message Infrastructure, Event Stream Processing (including Complex Event Processing or CEP), and Data Visualization.
Striim combines three of these classes of IMC technology (an In-Memory Data Grid, Event Stream Processing, and In-Memory Analytics and Data Visualization) in a single platform. This powerful combination of technologies allows us to support both Real-Time Data Integration and Streaming Analytics use-cases without customers needing to piece together a mish-mash of open-source or proprietary solutions.
These pieces come together quite naturally in our platform, and we have done the hard work of ensuring they function harmoniously together. The core of our platform, and the beginning of any solution, is our High-Speed Messaging Infrastructure and data collection capabilities. This is what enables us to collect data from many different types of sources – including enterprise databases through Change Data Capture, files, Hadoop, messaging systems like JMS & Kafka, and sensor data – and distribute it around a cluster for scalable processing.
All of the processing is performed through Event Stream Processing using in-memory continuous queries that you write in our SQL-like language (TQL). It is our intuitive UI and this TQL abstraction (no coding necessary) that makes our platform consistent and easy-to-use. These queries can carry out the filtering, transformation, and aggregation necessary for Real-Time Data Integration; and perform the Complex Event Processing, correlation and predictions necessary for Streaming Analytics. If you need to enrich the data (which you almost, always, do) then our built in In-Memory Data Grid enables you to load large amounts of reference data (from a database or Hadoop, for example) and add context to streaming data. This is referred to as “context brokering” – combining related or correlated data together to provide sufficient information to empower a decision. As we have said many times before, we started Striim with Context as one of our critical components, and it’s hard to over-emphasize the importance of Context.
The results of this processing can be written externally to databases, the cloud, messaging systems like Kafka, or stored in files or Hadoop. They can also be stored internally in-memory or within our pre-indexed searchable embedded ElasticSearch cluster. These results can power easy-to-build real-time streaming dashboards, which can rapidly give you insight into all your processing and analytics through Data Visualization.
It’s an honor to be a “Cool Vendor” and a great validation of the approach we have taken with our platform. We believe that converged platforms such as ours are essential to the success of streaming integration and analytics use-cases, and that you cannot perform analytics without access to streaming data through integration capabilities. It is also important to remember that such platforms do need to be enterprise-class. Things like security, reliability, recovery and scalability should not be an afterthought.
 Gartner “Cool Vendors in In-Memory Computing Technology, 2016” by Massimo Pezzini, W. Roy Schulte, Yefim V. Natis, Terilyn Palanca, 25 April 2016.
Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.