Big Data Streaming Analytics – A Leap Forward!
Here at Striim, we have been living and breathing Big Data Streaming Analytics for four years now. We believe that no Enterprise Data Strategy is complete without Streaming Integration AND Streaming Analytics. In fact, we are successfully helping organizations of all sizes discover the benefits of leveraging streaming integration and intelligence (the two i’s of Striim) to deliver the real-time insights they need.
I therefore find it very encouraging that some of the world’s most respected analysts are also seeing value in this space. Recently Forrester Research published, “The Forrester Wave™: Big Data Streaming Analytics, Q1 2016.” 15 vendors were covered in this report, and it is encouraging to see how thought around this space has matured.
An example of this is the importance of Context. In the latest report, there are a dozen mentions of “context,” including in the subtitle of the report: “Streaming Analytics Are Critical To Building Contextual Insights For Internet-of-Things, Mobile, Web, and Enterprise Applications.”
We started Striim with Context as one of our most critical objectives, and the importance of Context cannot be over-emphasized. Most often the raw data feeds derived from enterprise databases via change data capture (CDC), log files, or IoT do not contain sufficient information to make decisions. In order to ready the data for querying, or to deliver relevant insights, it is almost always necessary to join the raw data with reference or historical information to add context. Striim has been architected from day one to perform this task without slowing down your data flow.
As a relative newcomer to the space, we were very pleased to be considered a Strong Performer in this report, and were impressed by the authors’ keen understanding of what we believe to be our top differentiators.
The only reference to Change Data Capture (CDC) in the entire report relates to Striim. In any streaming architecture, the most effective way to extract real-time information from enterprise applications is to capture the change in their underlying databases as it happens. Whether the application is an in-house CRM solution, Billing System, Point of Sale, or ATM Transactions Processor, the end result of the application is to update a database.
Most DBAs strictly forbid running SQL against a production database, so if you want to know what’s happening in these applications, without having to intrusively modify them, you need CDC. Striim is the only streaming analytics platform to provide CDC as a fully integrated component of the platform.
We believe that Streaming Integration is a pre-requisite for Streaming Analytics, and a platform isn’t complete without it. As such, we have ensured that we provide a great number of data collectors (including CDC and IoT) and targets (including Kafka and Cloud), and we made the internal processing of the data easy through our SQL-like language.
We found it extremely astute that the Forrester report cited Complex Event Processing (CEP) capabilities.. This is the ability to spot patterns of events over time across one or more streams; patterns that may indicate something important is happening. We believe that CEP won’t survive as a standalone technology, and is instead a key component of any streaming analytics platform.
There is one aspect of our product that wasn’t highlighted, and that is Streaming Visualization. Anyone who has tried it knows that it is extremely difficult to build dashboards and reports to truly analyze your streaming data in real time, unless that capability is integrated into the platform.
Striim’s real-time dashboards can be built easily using a drag-and-drop interface, and rapidly deliver insights into your analysis. You don’t even need full-blown analytics to use our visualizations. We have customers, for example, who are performing streaming integration from enterprise databases via CDC to Kafka, who simply want to monitor this integration and drill down into specifics through our dashboards.
If you are thinking about Big Data Streaming Analytics, it is important to consider the entire eco-system. The actual analysis part is, in fact, a small piece of the puzzle, and requires that you can first collect, process, enrich and correlate the data in a real-time fashion. Once you have analyzed it, you most likely also need to visualize and report on it, and send alerts for critical events. It’s hard to piece together multiple technologies to achieve this, or to focus all of your efforts on coding when you would rather empower your analysts. Instead, please consider a single end-to-end streaming analytics platform, like Striim, that enables all of this, and more.