jSonar
Accelerating NoSQL Analytics

Enterprises today are struggling with the execution of Big Data analytics programs owing to challenges with unfamiliar system integrations and application development requiring new technologies and skillsets. While the promise of Big Data continues to be clear and compelling, the path to successful, efficient, and timely execution remains difficult. jSonar, a provider of JSON-native Big Data solutions, is solving this impediment by merging the simplicity of NoSQL, the speed of MPP platforms, and the scalability of Hadoop to deliver Big Data NoSQL analytical solutions that are agile, powerful and affordable. “We leverage the key attributes of the NoSQL paradigm, namely simplicity and high productivity, to enable consumable analytics within the Big Data domain,” explains Ron Bennatan, Co-Founder and CEO, jSonar.

The company’s core offering, SonarW, is a NoSQL Big Data platform that enables application teams to rapidly integrate an analytics engine that delivers high performance execution of complex queries, analytics, and machine learning tasks across TB and PB-sized datasets. SonarW’s NoSQL approach is fully complementary to databases such as MongoDB, MarkLogic, CouchBase, and DynamoDB and is easily integrated to provide analytics and archiving functionality. The combination of SonarW and the existing OLTP system creates a powerful and cohesive platform for addressing the full spectrum of query workloads, while preserving existing investments in software and BI tools.

SonarW is JSON-centric and thus highly flexible to process and normalize any form of data. This enables a high level of productivity by allowing users to interact with a variety of data without having to constantly reconcile their data structures. “JSON is a richer format than relational systems and better suited to deal with today’s and tomorrow’s more complex data structures,” asserts Bennatan. In addition to the analytics capabilities, the platform also provides visualization, gateway, and productivity tools to developers and analysts to facilitate usage. To simplify the transition of users from the SQL world to NoSQL, jSonar also offers a SQL layer that can translate SQL queries into NoSQL aggregation pipelines and convert the JSON result set for relational use cases.

SonarWe, a derivative of SonarW, is targeted at enabling high performance analytics for embedded systems that rely upon very small hardware footprints.

We leverage the key attributes of the NoSQL paradigm, simplicity, and high productivity to enable consumable analytics within the Big Data domain

“We saw that application vendors have a growing need for incorporating embedded analytics while minimizing HW needs,” elucidates Bennatan. “SonarW is highly scalable and can deliver impressive performance even while using a single processor core and limited memory.”

Another derivative product, SonarG, focuses on database activity monitoring and is optimized for data aggregation, storage, and reporting for Database Activity Monitoring environments. SonarG consolidates TB’s of highly granular activity information into a centralized audit warehouse while reducing operational costs and simplifying the data collection process. The net result is improvements in visibility, security and compliance initiatives by enabling various stakeholders to securely access relevant data. In addition to database monitoring, “SonarG complements a variety of other security applications and enables clients to consolidate much more than database events,” notes Bennatan.

These solutions highlight SonarW’s flexibility, ease of integration, and industry leading analytical performance, making it suitable for application in a multitude of industries such as IoT, security intelligence, and mobile analytics. HP, Schneider Electric, and Northrop Grumman are some of the esteemed clientele that have embraced the jSonar offering. As JSON and NoSQL continue to see wide spread adoption, the SonarW low-cost, high performance platform is a natural complement to a broad range of applications in search of efficient and effective analytics.