How a Unified, Real Time Data Platform Can Facilitate Real-Time Analytics Success

neub9
By neub9
6 Min Read

The demand for real-time analytics now spans nearly every industry, where every transaction, operation, and decision are characterized by its speed and freshness. While the push for real-time is evident, actually achieving a real-time data enterprise requires the restructuring of the data architectures.

John O’Brien, CEO and principal advisor at Radiant Advisors, and Lalit Ahuja, chief product and customer officer at GridGain, joined DBTA’s webinar, Modern Data Architectures for Real-Time Analytics, to examine innovative data architectures for real-time analytics.

O’Brien introduced Radiant Advisors and Unisphere Research’s 2023 market study of modern data architecture trends which highlighted the three main drivers for real-time analytics:

  • Increasing operational real-time analytics (49.5%)
  • Enabling AI and ML analytics use case adoption (48.6%)
  • Increasing analytics performance, scalability, and agility (47.1%)

Furthermore, a large number of respondents (50%) felt that real-time analytics will be the most valuable to their companies over the next five years. This widespread sense of value illustrates the modern demand for speed, performance, scalability, and flexibility when dealing with analytics.

If the perceived value is high, what’s the cause of its slowed adoption? The market study found these challenges as the top obstacles toward achieving real-time analytics:

  • The volume of real-time insights to downstream applications (55%)
  • Selecting a database capable of ingesting and analyzing data in real-time (51%)
  • Compatibility of real-time analytics platform with data management of analytics tools (48%)

O’Brien offered Radiant’s logical reference data architecture, which employs a unified real-time platform (URP), to centralize logical capabilities from a single place. A URP abstracts away the hard decision-making that happens when attempting to optimize a data architecture for real-time analytics.

“With real-time platforms bringing your data architecture in, your streaming, your analytics, your access, it will cover many different boxes,” he noted.

O’Brien explained that there are three major takeaways to consider when attempting to overcome these challenges and adopt real-time analytics:

  • Adopt a real-time mindset. Leave batch-oriented thinking behind and support internal users, customers, and partners with insights on up-to-date data and analytics.
  • Invest in modern data architecture. Seek vendor platforms that combine the most capabilities and flexible configuration for platform stability.
  • Build agile solutions that scale out. Enable data product teams to quickly build and deploy data and analytics on a high-performance platform with shared data.

Ahuja echoed O’Brien’s examinations, noting that GridGain’s customers are increasingly pushing for the ability to apply real-time analytics to a myriad of different business operations.

Explained as the “feeding-frenzy of data processing,” Ahuja pointed out that the explosive growth of data—which is fueled by a perfect storm of AI, IoT, 5G, and social media—is a major driver toward real-time analytics. With the implicit expectation that data will be accurate, fresh, and instantaneous, enterprises are left to deal with these assumptions by going real time.

Implementing a real-time data architecture means that businesses must face the dynamic, multi-dimensional problems of their data. Between data processing and aggregation across silos, latency due to data movement, executing high-performance compute in real time, and keeping up with the volume of data, there are many solutions that solve one or two of these problems—but not all of them.

Ahuja then introduced the GridGain Unified Real-Time Data Platform, which fills the need for a unified platform that addresses these multi-dimensional data problems simultaneously. The GridGain platform:

  • Stores the data, conducts stream processing, and hosts application logic and various types of analytics
  • Executes complex workloads against streaming and transactional data
  • Integrates with multiple systems of record and makes curated data available in real time
  • Enables advanced analytics and ML/AI-based decisioning in real time
  • Achieves sub-second latencies at massive scale

With GridGain’s URP, organizations can radically simplify their data architectures while optimizing it for their real-time needs.

“GridGain provides a unified, real-time platform [that] gives you the ability to process transactional data, to process streaming data, to process historical, and analytical data [while] all in real time being able to execute complex AI and ML models on top of it…and the data can be sitting anywhere,” said Ahuja. “GridGain can sit in front of all of these stores as a data hub to make that data available for processing across these different systems of record…thus enabling faster processing, faster execution, and faster analysis of data for all sorts of enterprise applications.”

For an in-depth review of trends and best practices for adopting real-time analytics, you can view an archived version of the webinar here.

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