Falkonry Service Improves the AI Experience: More Connections, Better Deployment Options

Falkonry Service was introduced a few months ago to improve the Falkonry solution fit and flexibility. As we’ve interacted with customers over the last several months, we’ve gained a greater understanding of the many different types of solutions people are trying to build with embedded pattern recognition capabilities provided by Falkonry. This release is a reaction to those needs and includes:

  • Improved data consumption capabilities
  • Expanded connection options
  • Simpler private deployment options

Falkonry Service Architecture

 

You can watch videos about Falkonry on our Website to learn more.

Improved data consumption capabilities

To better address current needs and in anticipation of future needs, we added a new core architectural element to Falkonry called Event Buffer.  Event Buffers separate the responsibility for managing data inbound to Falkonry from Pipelines that process that data.  One obvious benefit from the addition of Event Buffers is that one data source can supply data to multiple pipelines. This capability can be used to simply allow reuse of previously loaded data or to support more complex real-time simultaneous pattern recognition scenarios. Each pipeline, for example can make independent choices on how to interpret and process the same data stream.  An additional capability associated with Event Buffers is the ability to chain Pipeline executions to each other – i.e. to route output from one Pipeline to an Event Buffer that feeds other Pipelines.

Event Buffers also support and provide a focus for a growing set of capabilities related to data consumption.  The new release, for example, allows users to supply data to Falkonry in ‘Data Historian style’ format – sets of points in the form of a <timestamp, tag, value> triple format.  This augments the tabular structure supported previously.  Likewise, JSON (line delimited) support was added to complement CSV.

Expanded Connection Options

The new release also makes it easier to connect to Falkonry and to embed it in your solutions.  New capabilities include:

  • MQTT connectivity
  • Webhooks connectivity
  • Updated REST API
  • Expanded set of client libraries
  • Updated Splunk plugin

The Falkonry UI makes it easy for an Event Buffer to subscribe to a MQTT topic or for a Pipeline’s outflow  to be published to a MQTT topic or a Webhook URL. Additional subscription and publication options will be added in the future.

Client libraries are now available for Javascript and Python and others (e.g. C# and Java) are under development.

The Falkonry Splunk App that makes it easy to bi-directionally connect Splunk to a Falkonry Service instance was updated and streamlined, and plug-ins for other data platforms are under development.

Simpler Private Deployment Options

While the Falkonry Sandbox provides a useful and effective path for experimentation and early solution development, private deployments are the primary way Falkonry gets delivered.  Private deployments can be either:

  • Virtual Private Cloud (VPC) Deployments: Falkonry deployed in customer specific VPCs on public provider infrastructure like Oracle, Microsoft Azure, Google Compute Engine (GCE), Amazon Web Services (AWS)
  • Private Deployments: Falkonry deployed on customer controlled compute and storage infrastructure.

To make installation much simpler, Falkonry now offers a downloadable option for Private Deployments.

Falkonry AI Augments Operational Decision-making

As extreme growth in the generation of live operational data continues, the need to build solutions that recognize patterns in this data grows in parallel. Falkonry is committed to making AI-based pattern recognition an easy to incorporate component of any operations-oriented solution, and this release furthers us down that path.

For more information, please visit www.falkonry.com