Full-Stack Streaming Data Applications for Real-Time Insights

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise tech. In this feature, Nstream‘s Aditya Chidurala offers commentary on full-stack streaming for data applications.

Organizations must make efficient, timely decisions to gain a competitive edge in today’s fast-moving business landscape. In recent years, streaming data has provided enterprises with continuous data that can be leveraged in collaboration with stored data. However, the typical data analysis approach requires multiple data systems, which adds to costs and latency. This limits organizations’ ability to capture the true, real-time value of data and make immediate automated decisions.

This is where streaming data applications built on full-stack application development platforms provide more value through the data pipeline. As data is generated, streaming data applications gather, model, assess, and provide insights about data in true real-time. This is significantly different from systems that store and process data at a later time – even if just for a few minutes. When it comes to informed, automated, real-time decision-making and resulting competitive edge, customer satisfaction revolves around the speed with which data is received, processed, and analyzed and insights are gleaned. 

Traditional data processing takes data from many sources (on-premises, at the edge, or in the cloud) and digests and stores the data in applications prior to its incorporation. The storage stage, often called “data at rest,” renders the data static in a database or a drive. While acceptable for many processes, when real-time decision-making requires immediate access to information, data at rest hampers its effectiveness.  

Full-Stack Streaming Data

Key Use Cases for Streaming Data Applications 

Nearly all industries can benefit from real-time streaming data insights – from retailers tracking inventory to transportation companies reducing fuel costs to financial institutions providing personalized recommendations. Fraud detection is another critical and growing use case that is well-suited for real-time data analysis. It requires the fastest insights, the ability to prioritize them, and the generation of rapid responses.

Batch-processed data is too slow, as the information about fraud detected can be outdated, and in some cases, it can take hours to identify and respond. Streaming data applications are faster and more effective at reducing losses and preventing future financial strains.  

Other common use cases include real-time customer 360, inventory management and tracking, and anomaly detection.  

• Real-time customer 360 gives companies an accurate, real-time picture of real-world customer experiences. These insights help to create personalized offers and recommendations. 
• Inventory management empowers companies with information needed to streamline the supply chain process for resource optimization.  
• Fast anomaly detection flags issues such as equipment malfunction or shutdown, transaction fraud, and damage to external events, so automated mitigation tools can react before irreparable damage occurs.  

Quickly Constructing Streaming Data Applications 

Developers with the ability to create streaming data applications in mere minutes instead of months can produce dramatic operational cost savings and quickly deploy technology across a variety of use cases that measurably impact a company’s bottom line. New, open-source, full-stack streaming data application development platforms allow developers to isolate events at a real-world object level and continuously perform stream-to-stream joins at scale.

Integrating platforms with well-known streaming data technologies, such as Apache Kafka, Apache Pulsar, and AWS Kinesis, helps enterprises obtain real-time business insight that can help automate and create better-informed decisions at network-level latency.  

Companies can reduce the months it takes to design, create, and test traditional architectures. This is true even with multiple, complex open-source data systems, UI frameworks, and application servers. There’s no need for stream processing, UI frameworks, or additional servers to build streaming data applications. 

Producing highly efficient stream data application outcomes at unprecedented scale is doable with today’s modern platforms. With a 10x faster time to value and more than 70% lower Total Cost of Ownership (TCO), building and deploying streaming data applications rapidly is beneficial to today’s enterprise organizations. 

The lower TCO compared to traditional architecture means organizations can reduce the complexity in the building and maintaining of streaming data applications. The reasons why? Reduced infrastructure costs and stream-to-stream joins that provide: 

• At-scale performance. 
• Better optimization of human resources from fewer engineering hours. 
• The elimination of hiring SMEs or paying multiple software vendors. 

Organizations can reduce infrastructure bills by up to 80% when scaling architecture on fewer nodes, servers, and data systems. Isolating streams and events at the real-world object level (assets, customers, IoT devices, etc.) versus executing an additional query every time new information joins a real-world object is now possible with these solutions. Now, organizations can keep data in motion at network-level latency throughout the application stack. This helps to free up engineering hours as just one integrated system needs to be maintained with 5x fewer connections.

Time is no longer wasted designing, building, testing, and maintaining complex, open-source data systems. Engineers can now spend precious hours on projects that benefit the company’s bottom line. Organizations with full-stack streaming data applications can also hire one software vendor rather than hiring multiple vendors or SMEs for each open-source data system. Together, these factors drive considerable cost reductions. 

What Makes it All Work 

There are three core technologies that make streaming data application solutions possible and make it easy to respond to real-world events in true real-time.  

1. Streaming APIs allow organizations to observe real-time outputs of business logic and stream incremental updates to API clients without polling for changes.  

1. Stateful services ensure streaming data applications possess the contextual information and data needed to take action when a new message arrives.  

1. Real-time UIs ensure users have an unprecedented 24/7 live view of their operations as broad or granular as needed.  

Streaming data applications built on a full-stack streaming data application development platform empower companies operating in an extremely fast-moving business environment to build applications that help them make decisions quickly with a complete view of their business landscape, both physical and digital, all while reducing costs and latency. Now, real-time insights can help organizations streamline critical business decision-making without waiting for data storage and processing.