Vitria VIA Analytic DataFlow – Accelerating IoT Analytics Development

ADF

The excitement around IoT and Analytics on IoT data has evolved from hype to a stage where customers are implementing systems that generate significant business value. Vitria has a unique vantage point on the IoT and Analytics marketplaces as a result of our history of several years’ work building our VIA IoT Analytics Platform. One of the most critical challenges we have worked on with customers is that building high value IoT applications requires the use of extremely complex tools that are beyond the reach of all but the most technical staff members. Based on this experienced, we have added a number of foundational capabilities to VIA to make the platform more accessible and efficient for solutions developers and business analysts. The most significant is Analytic DataFlow (ADF).

Existing Big Data Tools are not Enough

To put ADF in context, it is useful to review the current state of Big Data and IoT tools. There have been significant innovations in several technology domains in recent years – including Hadoop, Cassandra, Kafka, and Spark. They have all played important roles in advancing the adoption of Big Data and IoT. The challenge is that these tools are very technical and are only accessible to developers with the narrow skillsets for each. This prevents non-technical experts from contributing to projects and overall progress. Companies are hampered by both the availability of developers with these skills, and it is very difficult for staff members with business knowledge to collaborate with them.

Analytic DataFlow – The IoT Analytics Development Accelerator

We have leveraged our long experience with enterprise, Big Data, and IoT development to address these challenges with ADF.

  • There is a need for a visual approach that works primarily for solution developers and business analysts instead of programmers
  • The solution needs to complement our unified analytics core. Our advanced analytics processes all types of analytics – real-time streaming, historical, predictive, and prescriptive – and then drives intelligent actions based on that analysis.
  • The visual approach needs to include some form of a library of building blocks approach to both jumpstart analytics application development and also enable customization.
  • The solution also needed to operate inside a larger framework that has the mission-critical capabilities robust enough to handle the demands of IoT.

ADF – Empowering Citizen Developers – Solution Specialists

Achieving success with IoT Analytics requires the empowerment of solution developers and analysts that have the highest degree of domain knowledge – but not technical skills associated with Big Data tools. The most valuable and innovative IoT Analytics applications and solutions will come from the minds and experience of the third group – the application practitioners and solution developers. ADF was built with this group in mind – and the goal is to transform them into ‘citizen developers’ and leverage their business knowledge to build value.

To learn more about ADF, go to the website here, or contact us.

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