Does changing the way we compute transform how we compete? In the last few years, with the emergence of new computing paradigms characterized by distributed analytics, large-scale data processing, and cloud-native infrastructures, the answer is nothing short of a resounding “yes”. Each of these paradigms is a disruptive force on its own when merged together, they have the potential to create a utopia of unconstrained digital innovation.

Vision: Unified In-Memory Computing Platform

When InsightEdge was first introduced as a standalone product a year ago, we set out to bring a decade and a half of high-performance computing innovation to widen the scope of in-memory data grid benefits from accelerating niche business functions to transforming these functions through insight and data science.
We charted an innovation path for enterprises, to help them leverage the intersection of fast data and microservices, where in-memory data grids are no longer just a middleware low-latency tier, but are a force multiplier, connecting insight to execution at sub-second latency.

Today, we’re delivering on this mission by putting it all together: InsightEdge Platform 12.2.
We know that in the age of fast data and cloud-native computing, competitive advantage is defined by an organization’s ability to out-innovate its rivals through becoming more digital, and more real-time. We believe that the efficacy of insight-driven innovation is best realized through the convergence of three fundamental components (see them below), that, when combined, can transform virtually every element of an enterprise.

As a result, insight-driven transformation can happen at an unprecedented pace, accelerating intelligent applications through streaming analytics and continuous machine learning. This shifts data-driven thinking from retrospective analysis (traditional big data) to just-in-time analysis and high-value decisions against live data. In such an enterprise, even the most elaborate forecasting analysis done over weeks can be trumped by a few machine learning models that can predict revenue with radically improved accuracy.

Reality: Limitation through design, not computation

Most companies design their transactional and analytical technology stacks against completely separate infrastructures, usually because of high-latency storage that inhibits convergence. Consequently, information is hoarded and decision insight is limited to the tribal knowledge of a few data analysts. Such design is a remnant of an era when disk I/O was the fundamental bottleneck that dictated information architecture. Architects end up focusing on minimizing bottlenecks, rather than maximizing insight and innovation velocity. Thus, digital innovation is limited by design, not compute limitations (which don’t exist anymore).

Source: Intel “In-Memory Computing and Real-Time Business Intelligence”

Reality, revisited

We’ve learned that one of the fastest ways for businesses to become insight-driven is to bridge the transactional-analytics gap through leveraging in-memory insight platforms. GigaSpaces InsightEdge Platform is a manifestation of this vision that allows both application developers and data scientists to innovate with confidence and at scale.

So, what are the key components?

Component 1:  Eliminate the three most expensive letters in fast data analytics: E-T-L by co-locating analytics and transactional workloads under one low-latency storage engine (over RAM, SSD and SCM), while giving flexibility to a multitude of data management and query APIs (SQL, graph, JSON).
Component 2:  Leverage cloud-native development patterns using microservices architecture through XAP In-Memory Data Grid for real-time applications.
Component 3: In-data integration of Apache Spark workloads and transactional data provide online machine learning and fast analytics against data in motion.
Ultimately, this represents a new and exciting computing paradigm that allows unprecedented speed and scale for applications that run the business and those that manage it to seamlessly integrate.

Towards insight-driven transformation

Think of a forecasting application taking hours to calculate demand that can now be done in seconds (right from within your customer-facing systems), or an IoT machine learning workload that continuously learns from live sensor data to predict equipment failure in real-time. We’ve essentially transformed applications into sales tools and automated intelligence, thereby changing the way we do business and out-innovate competitors. This is a small example of how GigaSpaces customers are harnessing in-memory computation for insight-driven transformation.

 Indeed, changing the way a business computes through an in-memory platform like InsightEdge, will fundamentally transform the way it competes. Ask yourself, what could happen if you transform the way your digital business computes?