Peter Hughes, Head of Cloud at Push Technology, describes how a data mesh enables real-time applications.

The Demand for Scalable, Real-Time Apps

Real-time is becoming the norm, but what does real-time mean? Withing real-time applications, timing is critical. Though there is no industry-recognized definition of the minimum time a “real-time” application must exhibit, it’s safe to say that the human eye should not recognize the program’s computational period. In other words, any response should be deemed instantaneous.

According to IDC, 30% of data in the global datasphere will be real-time by 2025. Organizations face the challenge of harnessing expanding and evolving data sources and the complex ecosystem in which they reside. This is particularly relevant to specific industries. For example, 44% of retailers say real-time retail is one of their top 3 priorities.

However, developers and software architects struggle to introduce real-time into their applications as they need to learn new skills and technologies to make it a reality. Undoubtedly, it’s a complex subject and challenging to implement effectively.

Traditional Streaming Solutions

Traditional data streaming tools provide ways to help unify and normalize distributed data sources. However, these tools are fundamentally built around polling-based methods (e.g., REST and SOAP) — approaches that are incompatible with the requirements for processing live data. In the diverse ecosystems of today’s digital world, architectures can include any combination of polling-based, event-based, and bespoke infrastructures — often with perplexing integration requirements.

Each integration introduces bottlenecks and other inefficiencies. The cumulative effect of all these integrations are applications which tend to be inflexible and difficult to scale. The very nature of building a complex system of loosely coupled components creates a situation counter to the goals of a real-time application.

Having said all that, organizations across the globe realize that data is the currency of competitive advantage. Therefore, the size of data packets, frequency of data transmission, data transmission and update speed, and the “intelligence” of data handling are critical to successfully running mission-critical, corporate applications and making time-sensitive business decisions. The core expertise of many companies lies in the development of applications, not in developing and optimizing streaming data technology. So, what is the solution?

The Benefits of an Intelligent Data Mesh

An Intelligent Data Mesh can manage, optimize, secure, and distribute live data, no matter the origin — providing intelligence on the network edge and a single source of truth for an organization’s information. Use cases exist across all industries, wherever there is business value in immediate data distribution: sports and trading feeds, geo-location data, IoT sensor streams, and many others.

Modern solutions need to combine the advantages of traditional streaming tools with the power of real-time. They join together both polling and event-based back-ends to provide a single unified platform for managing and distributing live data.

In doing so, this removes the traditional constraints of data management, allowing businesses to create new revenue channels from pre-existing infrastructure while greatly simplifying the development of new and innovative applications. The key outcomes of deploying an Intelligent Data Mesh should be efficiency, scalability, and cost-effectiveness.

The Issue of Scalability

With so much to organize, centralize, and connect, the need to create and publish new APIs is often relentless. Beyond the customer-facing implications, there are also business partners and internal stakeholders who need to interact with data and applications regularly.

A main issue developers and software architects face when building real-time applications is scalability. Scale is maintaining and ensuring reliability and performance as your user base grows. Simply throwing hardware at a scalability issue is not the answer.

When designing a system, scalability should be one of the first things an architect considers. However, building a scalable, globally available, low-latency platform or system that provides service level and performance guarantees is not easy.

Solving the Scalability Challenge

The value of an Intelligent Data Mesh is its ability to manage real-time data and how efficiently and effectively the solution handles the scale of distribution. The wide array of corporate applications require different types of scale, including the ability to serve large and often variable client volumes, handle tens or hundreds of thousands of unique data streams, and provide high throughput of data across geographically dispersed or remote regions. REST-based approaches often require large numbers of server instances to support heavy traffic loads, plus the associated operational complexity of coordinating data and monitoring systems.

An Intelligent Data Mesh is purpose-built to deliver optimal performance and reduce operational risk across both axes of scale-traffic volume and data throughput — regardless of fluctuating conditions.

Let’s Not Forget Security

Of course, when APIs need to be accessible to so many different parties, it goes without saying that security is a significant focus. Access control is a core concern for any business moving data. Exposing real-time data feeds can introduce additional operational risk if not managed correctly. Simultaneously, the ability to expose real-time feeds enables a substantial opportunity for revenue growth.

An Intelligent Data Mesh can act as a single access point to deliver centralized security control over real-time data. Using a pluggable authentication system allows businesses to enforce identity control across all data using whatever mechanisms are required — SSO, LDAP, or custom authentication — with fine-grained permissions and dynamic authorization for granular access control to easily grant or revoke privileges, as required.

Conclusion

Developers and software architects often struggle with the complexities of creating real-time web, mobile, and IoT applications. A key challenge they frequently face is managing and integrating different systems across both back-end and front-end codebases. Most developers know how to create performant back-end systems. However, when it comes to pushing data out into different environments, over sometimes congested or unreliable networks such as web, mobile, or satellite networks, tool options are limited, and development becomes far more complex. This is when an Intelligent Data Mesh has a key role to play.

Peter Hughes

Peter Hughes is an experienced senior engineer in high-performance Java applications and has led the design & development of both back-end and front-end projects for real-time data platforms. He is currently Head of Cloud for Push Technology’s Diffusion Intelligent Data Mesh. The solution powers real-time, highly-scalable, and mission-critical web, mobile, and IoT applications for companies worldwide.