Push Technology is proud to share the enhanced product vision through the release of Diffusion 6.6.
We are introducing the new name Diffusion Intelligent Event Data Platform. We classify the capabilities of Diffusion into 3 key components: Data Gateway, Data Wrangling and Data Distribution.
This is the full Diffusion 6.6 release. Some features added since 6.5 were previously available in the 6.6 Preview 1 and Preview 2 releases; in some cases these features have been substantially altered and improved since the preview.
Data Gateway component facilitates the consumption of data from a variety of sources and in disparate formats and publishing the processed event data into Diffusion Platform. Data Gateway includes adapters to connect with a variety of data sources. The vision for Data Gateway includes capabilities for transformation of formats, computation to add business logic and more.
Following are the capabilities added to Data Gateway component in Diffusion 6.6:
You can now control these adapters and visualize their activity using the management console.
The Kafka adapter in this release offers more configuration options than the preview version, including the ability to configure how imported topics are structured, and to import topics that match a regular expression. The adapter can translate data from Kafka topics to Diffusion topics, and from Diffusion to Kafka.
The Java Message Service (JMS) adapter has been updated to use a JSON configuration format.
Support for Diffusion topic subscription is added, which allows the updates to Diffusion topics to be sent to JMS destinations.
The adapter can be visualized and monitored in the Diffusion management console as well.
See JMS adapter docs for details.
Diffusion now supports the popular MQTT messaging protocol, enabling MQTT devices to publish and subscribe to Diffusion topics.
Now you can gather and distribute real-time data to Internet of Things (IoT) devices and other remote hardware, while still making use of Diffusion’s advanced data-wrangling and security capabilities. There is no need to install any Diffusion code on the remote devices.
Data Wrangling component provides the capabilities to design the Topic Tree structure and access controls to route the event data for further distribution. These activities can be performed via the Management Console or the SDKs. The Data Wrangling component has rich set of capabilities to dynamically transform the event data as well as the Topic Tree design.
Following are the capabilities added to Data Wrangling component in Diffusion 6.6:
Topic views are a dynamic way to map and transform a set of topics, creating reference topics in another part of the topic tree.
Some topic views can create reference topics with paths that depend on the values of source topics. This means that if the value used to determine a reference topic path changes, the reference topic gets removed.
There is now a new option to preserve reference topics derived from a value, even when the value changes. This makes the set of reference topics generated more stable.
See the preserve topics clause docs for details.
By default, when a topic view derives a reference topic path from a source topic value, if the value contains a / character, it is treated as a path separator.
For example, a value like “Gold/Silver Index” could create a branch called “Silver Index” under a branch called “Gold”, making the topic path longer than required.
Diffusion 6.6 introduces a new option to replace / with a string of your choice when creating reference topics.
See the separator clause docs for details.
Topic view inserts are a new addition to the data processing capabilities of topic views. Topic views enable you to mirror selected source topics to another part of the topic tree, creating reference topics.
With a topic view insert, you can now merge data from topics other than the selected source topic into JSON reference topics. You can insert whole values, or partial data specified with JSON pointers.
Advanced features include the ability to derive paths from values, and chaining multiple inserts.
You can now update a time series topic via the standard topic update API, treating a time series topic as if it were a single topic with the same event type as the time series. This means that when you update time series topics, you can now use features like update constraints, update streams and the addAndSet operation.
In addition, you can now create a time series event with a custom timestamp of your choice, instead of one based on the current time. You could use this to load historical data into a time series topic, or for testing purposes.
Data Distribution component facilitates the massive scale of event data transmission around the globe in milliseconds, with secure access control to assure reliable delivery of the exact data, in the correct format that each recipient requires.
Following are the capabilities added to Data Distribution component in Diffusion 6.6:
Your application can load a smaller core client bundle, and load additional features dynamically as required.
The Python SDK enables you to develop Diffusion clients in Python, with support for core features including subscribing and publishing. Some features available in other SDKs are not yet supported in Python.
See the Python SDK overview for details.
Diffusion Management console brings all the 3 components together to deliver the single view of the Diffusion Intelligent Event Data Platform.
Following are the capabilities added to the management console component in Diffusion 6.6:
The Diffusion management console now supports integration with third-party single sign-on systems.
See Configuring the Diffusion management console for details.
When you’re using Diffusion’s high-availability capabilities, you can now see the status of your server clusters from within the management console.
Whether you are a new customer or are already experiencing the power of Diffusion, we hope you enjoy the rich functionality of Diffusion 6.6 as part of your event-driven application. It is available for immediate download today.