Data at Rest, Streaming Data, and Streaming Data Applications 

Are you making the most of the data at your disposal? Concerningly, many companies looking to move, interpret, and act on streaming data in concert with data at rest use complex application architectures that make it difficult to surface insights for real-time decision-making.

The good news is that new technologies have emerged to help businesses maximize their data’s value at rest and stream data under one sturdy roof. End-to-end streaming data applications, specifically, offer a way to achieve real-time visibility, contextual analysis, and automation.

Data at rest vs. streaming data

Before discussing how to gain visibility into your entire in-motion and at-rest data landscape and maximize the combined value, let’s first define what these two terms mean.

Data at rest

Data at rest refers to static data, such as data on a hard drive or in a database. This data is not being actively processed or transmitted but is instead stored and waiting to be accessed or manipulated.

Generally speaking, data at rest is more suitable for batch processing, where large amounts of data are processed at specific intervals (e.g., daily, weekly, monthly, etc.). This is why data at rest alone is not useful for unlocking real-time situational awareness, insights, and action.

Streaming data

Streaming data refers to data generated continuously and transmitted in real-time, such as data from supply chain updates, geolocation updates, point-of-sale transactions, telemetry sources, or social media feeds. This data is typically transmitted in small, continuous packets or streams and can be consumed and processed in real-time by downstream systems.

Streaming data is best suited for real-time processing and analysis, where immediate actions can be taken based on incoming data. For many use cases — such as asset tracking, fraud detection, and personalized offers — batch processing of data at rest is too slow, as the insights generated are outdated or no longer relevant by the time they are received. These scenarios demand the real-time decision-making that streaming data enables.

Other challenges that businesses face when trying to leverage the full value of their streaming data include:

• Overreliance on batch processing and a “store-then-analyze” approach.
• Prohibitive costs associated with processing multiple streams of data.
• The complexity of managing multiple data systems, vendors, and SMEs.
• Insights that are not truly real-time due to latency.

Traditional data applications vs. real-time streaming data applications.

Organizations can employ two main approaches to process, analyze, and act on their streaming data, either on its own or in tandem with data at rest: traditional and real-time streaming data applications. Here’s how they compare and contrast.

Traditional data applications

Traditional data applications typically utilize a stateless architecture and involve many complex pieces that introduce potential points of failure and added latency. They are useful for helping companies move data from point A to point B (e.g., from a streaming data source to another place, such as a database). However, the actual value extraction for the business — processing, analytics, and automation — happens once the data is at rest in the database. Traditional applications don’t actually run on streaming data, they run on data at rest.

The ongoing management of the data systems involved in these architectures can quickly become time-consuming and expensive. They also require system experts to manage and maintain. What’s more, latency compounds when data has to be polled and pass through multiple systems, making it difficult to execute critical business decisions within time-sensitive windows of opportunity.

The other compounding challenge is that these applications depend on database querying to access the context required to understand and act on the current state of the business. Because only the database knows when any of its data has changed, users must execute queries repeatedly, wasting database resources and their own. High volumes of (often unnecessary) polling and query compute increase cost and latency, making it extremely difficult to complete the “last mile” of enabling end users and systems to take action on the data in a timely manner.

Real-time streaming data applications

Many companies still struggle to make full use of their streaming data and surface actionable insights in a time frame where they can make a difference. Decision-makers cannot close the loop on seeing, understanding, and acting on the key changes happening within huge amounts of constantly moving data streams.

Streaming data applications provide full visibility, context, and automation of streaming data all the way through the application layer to ensure insights make it to relevant users and systems in a timely manner. These entity-based apps involve a stateful architecture and as few moving pieces as possible to reduce latency, improve cost efficiency, and optimize data locality.

Streaming data applications allow organizations to build a complete, stateful model of the enterprise to identify noteworthy changes, understand what they mean, and take action accordingly. Due to streaming data apps’ stateful services and streaming APIs, context is automatically updated at all times, and business logic (aka essential rules and domain knowledge) is run as soon as data is received.

Business logic results are continuously pushed to downstream systems, such as real-time user interfaces and enterprise integrations that drive automation (e.g., a customer experiences a service issue so a support agent proactively reaches out to resolve the problem). Examples of common use cases for real-time streaming data applications include real-time:

• Customer 360: Personalized offers, proactive support, etc.
• Live marketplaces: Ride sharing, on-demand delivery services, etc.
• Operational visibility: Anomaly detection, inventory management, etc.

Unlike traditional data applications, streaming data applications analyze data at rest concurrently with real-time streaming data (from sources such as assets, equipment, devices, etc.) to derive the contextual insights required to drive decisions seconds after an event has happened.

In other words, streaming data applications take both streaming data and static data as inputs during ingestion. Once everything has been ingested, the data is streamed to all recipients and held in memory while still useful. When new data comes in that must be evaluated against existing information, this contextual data is already part of in-memory state, which means no last-minute database queries or streaming joins are required to retrieve the data.

With traditional applications that use a stateless model, the context required for decision-making is not already in place and needs to be retrieved via database querying or some form of streaming join. This drives up latency beyond the acceptable range, and any insights derived are no longer real-time.

Using Nstream to maximize the value of streaming data

Nstream dispels the myth that it must be difficult and costly for organizations to use streaming data apps and execute mission-critical business logic with low latency. Built on SwimOS, the full-stack Nstream Platform is open-source and the fastest way to build streaming data applications that enable real-time visibility, live modeling of complex business operations, and responsive decision automation at scale.

Nstream closes the gap between data at rest and streaming data by running business logic directly within stateful objects so that applications can run at the speed of their fastest data sources. In addition to eliminating the complexity of managing multiple data systems, Nstream enables scalable stream-to-stream joins. This allows organizations to combine multiple important pieces of contextual information to enhance decision-making. For example, a retailer could combine user transaction data with e-commerce browsing data and location information to make more relevant, timely offers to customers when they are ready to shop. Performing stream-to-stream joins with other technologies is exorbitantly expensive, even at a lower scale.

Join the evolution of streaming data applications.

To meet today’s business needs, streaming data and at-rest data need to be pulled into the application layer via streaming data applications. It’s here that modern digital transformation is happening with application-level depth, including real-time processing and situational context for decision automation. Whereas most application services are still designed for database-driven web applications, Nstream provides a way to evolve the present — and into the future — of streaming data applications.

To learn more about how Nstream compares to other popular data streaming tools, check out Data Streaming Technologies: Which Is Right for My Business?