At the opening of a new year for Apama Streaming Analytics and before we continue improving and adding functionality to Apama, we wanted to take a quick look back at what we achieved. Over the preceding 12 months we have added many features and improved Apama in various ways. The detailed changes are published as part of the releases we make, but in an effort to publicize and demystify features we also publish regular blog posts on the apama community website ( www.apamacommunity.com ) which include samples and sometimes videos!
There were a few strong development themes last year that drove the content of the blog and I would like to point out some posts considered to be the highlights.
The ability to write extensions to the Apama language in Python was an oft-requested feature that we delivered in the 10.3 release. Python has a large amount of third-party libraries that cover many different domains and by adding this feature we allow users to use that functionality from EPL. Specific interest was shown in using machine learning libraries such as TensorFlow and Scikit-learn and so we created a set of posts that would take you from the basics through to using one of these libraries.
A second theme was our support for containerisation in which we added facilities for creating images from user applications. These images can then be used in Docker Swarm or Kubernetes gaining the advantages of scalability, availability and management that these environments provide. Again we take the user through the process in the following series of posts.
Additionally we saw that we could utilise containerization to help users produce more complex applications
The year culminated with the release of our images on AWS Marketplace. Our Images are usable on both ECS and EKS and instructions are contained in this blog:
The AWS platform also has many other capabilities that could be used by Apama via the HTTP connectivity plugins.
The last post I want to highlight contains details on how you can monitor and visualize your applications in Apama by using the new capability to expose metrics to Prometheus. It specifically combines using Prometheus with multiple instances of Apama running in containers in Docker. It then displays the data using Grafana which facilitates graphical displays such as scrolling graphs and gauges.
For a list of community contributions and projects we have a github page ( https://github.com/ApamaCommunity/Creations-Index ) on this page you will find some interesting uses of EPL, common codecs and plugins for Apama. If you have something you want to share please check out the instructions on github and fire off a pull request to get it added.
The only thing left is to wish everyone a happy and productive new year!