StreamAnalytix

Impetus StreamAnalytix is an analytics platform for both real time, streaming data and batch processing. It operates on the principle that open source technologies in the streaming analytics space (for example, Apache Kafka and Apache Spark) are in many cases mature enough to offer significant advantages over proprietary solutions (such as future proofing and community support), but that orchestrating and integrating several of these technologies together into a complete, enterprise-ready solution is complicated, slow and expensive. Therefore, Impetus has chosen to build StreamAnalytix on top of popular open source technologies, combining them into a single offering within the product, and saving you the trouble. It does this while providing enterprise-level scalability and support. In essence, it is an open source powered, enterprise-grade platform.

More than that, StreamAnalytix is not just an analytics product. All told, it provides end to end, 360-degree data processing, including ingestion, cleansing, transformation, blending, loading and visualisation (via real time dashboards), as well as, of course, analytics. It is available on-premises, in-cloud, or as part of a hybrid solution. In addition to StreamAnalytix, a freemium offering, Visual Spark Studio, is also available.

Customer Quote

“StreamAnalytix enabled $5m annual savings in call centre costs.”
Leading wireless & telecom services provider

StreamAnalytix allows you to build data pipelines and analytics applications using a visual UI (user interface) by dragging and dropping blocks of pre-built, integrated functionality onto a canvas and arranging them into a model. This process is very simple, and allows you to very quickly build out use cases into functional applications. It can be used to build batch, micro-batch, and event-based (in other words, streaming) analytics applications, and also features a built-in testing interface. Moreover, this UI, along with the streaming applications you can build with it, are treated by StreamAnalytix as abstractions on top of the underlying streaming engine you are using. This is important for two major reasons: firstly, it means that StreamAnalytix can support multiple streaming engines, presently including Spark, Storm and Flink; and secondly, and arguably more importantly, it means that the streaming engine you are using can be switched out without any change in either the front-end interface or your existing streaming applications. This makes it relatively simple and straightforward to change your streaming engine, and it enables you to do so without retraining staff to use a new interface or rebuilding existing streaming applications. Additionally, StreamAnalytix can be installed on and integrated with any existing streaming analytics deployments that you might have.

StreamAnalytix also features extensive support for machine learning and predictive/prescriptive analytics. The platform allows you to train your machine learning models using either batches of historical data or in real time via streams, then deploy those models in real time to drive analytics and scoring for both batch and stream processing. StreamAnalytix supports a variety of model management techniques, including A/B and Champion/Challenger testing. Moreover, it also offers support for ‘hot swapping’ of models, meaning that whenever a model in training becomes more effective than a model in deployment, the two can automatically be switched over, so that the model that’s in use is always the most effective model you have. The product supports a variety of machine learning algorithms, including SparkML, TensorFlow and Python models. PMML (Predictive Model Mark-up Language) is supported for model portability.

In addition to StreamAnalytix, Impetus also offers Visual Spark Studio. This product is a freemium, stripped down version of StreamAnalytix proper, suitable for building creating, testing and running Spark applications. It is freely available for a single server or desktop.

The streaming analytics space is all about speed. By making the analytics process faster, organisations seek to make more responsive, more effective and more timely decisions, ultimately trying to react in seconds, not hours, to the data that is entering their system. This is necessary not just to meet the demands of your consumers, but also to compete with other organisations who are meeting those demands.

This need for speed has led many vendors in the space to adopt either micro-batch or event processing, and in the process move away from old batch processing methodologies. However, as it turns out, many organisations still want and need batch or micro-batch processing in addition to event processing. Accordingly, Impetus has chosen to offer all three as part of StreamAnalytix. Moreover, they do so via a unified platform that can manage all three options under one roof. In fact, this is one of the major strengths of StreamAnalytix: the ability to interact with a multitude of different types of analytics (event, batch, micro-batch) and streaming engines (Spark, Storm, Flink) using the same interface and applications.

This inclusiveness might lead you to think that StreamAnalytix isn’t fast. However, this is far from the case. In fact, the product has been benchmarked and tested at one million events processed per second. Additional features are also in place to ensure this performance is maintained, such as alerts that fire if processing speed falls below a certain threshold.

The Bottom Line

StreamAnalytix provides an excellent solution for streaming analytics that combines the strengths of open source with the reliability, manageability and support of an enterprise solution. Together with the end to end data processing, machine learning and batch processing capabilities it offers, it is a powerful solution not just for streaming analytics, but for analytics as a whole.