What will be the I/O layer for generative AI? Will it forever be natural language chat? Or is chat just the degenerate case of a much broader tapestry of artificial linguistics?

Enterprises today demand more value from their streaming data in less time. However, their existing architectures for streaming data applications make it virtually impossible to analyze and act on their data at scale and in real-time without compounding latency and cost.

Let’s cut to the chase — traditional streaming data application architectures are not feasible for modern business needs because they are complex, resource-intensive, and difficult to scale.

In Q1 2023, our company rebranded from Swim.inc to Nstream. The goal of this shift was to align better the company, our culture, and our value proposition with the evolution of streaming data applications. While our name and brand have changed, at our core, we continue to offer customers the ability to build streaming data applications in minutes, not months.

Companies require actionable insights from the vast amounts of data they gather. However, implementing a data processing technology stack to effectively deliver those insights and execute complex use cases in a timely manner quickly becomes prohibitively expensive.

The Value of Nstream’s Real-Time UIs vs. Traditional UIs

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Data at Rest, Streaming Data, and Streaming Data Applications

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Common Streaming Data Application Use Cases

Nstream mentioned in “Unified Real-Time Platforms” developing its own native stream processing capabilities

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Another day, another round of tech layoffs. From giants like Alphabet, Amazon, Meta, and Salesforce to startups and scaleups, tens of thousands of workers have recently been dismissed from their positions. Tech layoffs in just the first half of 2023 have already eclipsed the total number of industry job cuts in 2022.

Are you looking for better ways to model your business’s ever-evolving state? Join Fred Patton, Nstream Developer Evangelist, as he walks you through a simple demo of how to build streaming data applications. He’ll demonstrate how to model your business, including all your digital and physical entities, whether you operate on-premises, in the Cloud, or both. The result: empowering you to make better contextual-based business decisions in real time.  This demo will cover the following:

• Overview of streaming data applications and how they can transform your business
• Real-time visualization and understanding of your business’s state
• Example of a real-time traffic application in Palo Alto
• Steps to build your streaming application to model your business operations

Many use cases that exemplify how we think about the future of business revolve around organizations’ ability to make sophisticated decisions with agility and speed. Consider customer 360 initiatives. A company might offer a specific individual a coupon code while they’re browsing the website or at the store. To do so, that company must have real-time visibility into its customer and company data and be able to send that coupon instantaneously.

Businesses need to see, interpret, and act on data in real time to remain competitive in the modern business landscape. That means implementing a tech stack that can process streaming data in a way that meets latency, cost, and business needs.

On its surface, business logic might look like a long list of tactical rules, procedures, guidelines, and best practices that guide thousands of small decisions a company makes daily. However, thinking of business logic like a Monet painting is helpful – all the small details add up. What might look like hundreds of tiny decisions up close, reveals a larger picture of how a company operates and makes critical business and customer decisions.

Why Streaming Apps Matter

Companies need to be nimble. With the rapid pace of change we experience today, quarterly — or even annual — planning doesn’t cut it. Competitive organizations need the ability to adjust by the day, hour, and even minute and second to accommodate market shifts.

As the volume of global digital activity and financial transactions continues to grow, instances of fraud have become more prevalent and more difficult to detect. Fraud detection is a major concern across industries because of what’s at stake. It’s critical for businesses to preserve reputation and revenue, reduce financial risk, and maintain high customer satisfaction.

Smart electrical meters, smart cars, smart cities — the smart power grid has never been in higher demand. The global adoption of electric vehicles (EVs) alone is expected to increase by 25% year over year until 2030.

The application layer will be the next domino to fall in the ongoing transformation from batch to streaming. And one particular quirk of streaming data—the distinction between state and change—will play an outsized role in determining the outcome.

The application layer will be the next domino to fall in the ongoing transformation from batch to streaming. And one particular quirk of streaming data—the distinction between state and change—will play an outsized role in determining the outcome.

Nstream Cloud PaaS is the Fastest Way to Build Full-stack Applications with Streaming Data; Early Access Now Open to Developers

Welcome to the evolution of streaming data. Nstream, formerly known as Swim, is ushering in a new era with its full-stack streaming application platform-as-a-service (PaaS) that helps businesses understand, contextualize, and act on large quantities of streaming data in true real-time.

The Nstream platform simplifies the development and maintenance of real-time streaming applications. Akka gives developers the opportunity to easily create powerful, message-driven concurrent and distributed applications.

Many organizations that practice stream processing utilize Kafka brokers, the storage layer servers that are used to host Kafka topics. They may also use Kafka Streams to build applications and microservices that unlock insights based on logical or mathematical relationships between data sources and their states.

Imagine you’re a credit card company and a charge comes in. All you know is that this charge is larger than the customer’s previous charge. Is it fraud or not? If it is and you allow it, you’ll lose money, and the fraud may continue; if it’s not and you deny it, you’ll frustrate or even lose a genuine customer.

Web Agents are the key to fulfilling the promise of digital twins: real-time observability at scale to drive smarter decision-making. With a history dating back to 1960s space exploration, the digital twin idea — using a virtual model of a physical object or system to better comprehend the present and predict the future — has been around for a while.

Microservices are the backbone of modern applications. They help businesses increase the speed of software development, seamlessly adopt new technologies as needed, and meet security and compliance mandates.

Simply defined, digital twins are virtual representations of physical objects. Whether duplicating a machine (like how every Tesla has a digital counterpart to allow for closer monitoring of systems), or a real-world process (like the tracking of changing traffic lights at an intersection), digital twins are created by streams of real-time data.

Databases are where real-time data streams go to die. They are a dam in the stream, preventing vital, real-time information from getting to the people who need to make critical business decisions in latency-sensitive time windows.

Imagine a world where software evolved the way broadcast media did: streaming first

What problem are you trying to solve? A data problem? Or a business problem?

The Internet vs. The Web

Digital twins are like a social media profiles for things

Latency is an issue that would upset a gamer playing online or frustrate a movie-watcher choosing between films. Unfortunately, it has become a normalized part of modern user interfaces in the business world. This issue is especially concerning for enterprise UIs, such as business intelligence dashboards because decisions are made using supposedly fresh data and insights that are not truly real-time.

Travel light and live off the land

Whether database, distributed, web, or mobile, all applications are built around a core set of underlying technological capabilities. Notably absent from this list is the ability to build real-time streaming applications.

Playdough can be molded into any conceivable shape. Lego is much more restricted in the forms it can take.

Storage is the leading cause of data loss in the industry

Choice: Eager or Lazy (a.k.a. Push vs. Pull)

What Is State?

Brokers don’t run applications - they are a buffer between the real world and an application that analyzes events. An event stream processor (in Apache Kafka/Pulsar terms) or dataflow pipeline is an application that, given the event schema, analyzes the stream continuously to derive insights.

Swim enhances the actor model to support continuous analysis of streaming data from millions of sources in a distributed runtime environment - using Java. Swim is the easiest way to build applications that continuously analyze streaming data from Apache Kafka.

Analyzing data on the fly is tricky: Data sets are unbounded and real-time responses demand fast analysis. Incremental algorithms can be used for statistical analysis, set membership, regression-based learning, and training and prediction of learning algorithms, amongst others. In specific use cases, domain-specific algorithms also apply.

Streaming data contains events that are updates to the states of applications, devices, or infrastructure. When choosing an architecture to process events, the role of the broker, such as Apache Kafka or Pulsar, is crucial - it has to scale and meet application performance needs - but it’s necessarily limited to the data domain. Even using a stream processing capability such as Kafka streams that triggers computation for events that match rules, leaves an enormous amount of complexity for the developer to manage - and that’s all about understanding the state of the system. Here’s why you care about the state of the system and not its raw data:

The rise of event streaming as a new class of enterprise data that demands continuous analysis is uncontroversial. What’s puzzling is the approaches being taken by the event streaming community to the storage of event data and the semantics they seek to achieve for event processing applications.