Julia Wiltshire | Nstream 28 Aug 2023
How Nstream Lowers TCO by 70%
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.
With Nstream’s simplified approach to building streaming data applications, companies can optimize core infrastructure and reduce ongoing maintenance expenses, even in today’s resource-constrained environments.
In fact, in an era of tight IT budgets yet growing technology expectations, companies can achieve a 70% decrease in the total cost of ownership (TCO) by building streaming data applications with Nstream. Here’s how.
The inherent costs of traditional streaming data application architectures
Streaming data applications bring streaming data to life by empowering organizations to collect, organize, and analyze data in a time frame that enables immediate action in response to real-time events. Examples of streaming data applications include customer 360 (e.g., personalized offers and recommendations), marketplaces (e.g., ride-hailing services, on-demand delivery services), and real-time visibility (e.g., inventory management, fleet management).
Many large technology companies have devised extremely complex data streaming architectures involving several open-source data systems to handle their streaming data at scale. However, managing a host of streaming data systems and their integrations is inherently more time-consuming and expensive, especially for enterprises without top technology talent. For example, expenses can pile up from any of the following:
- Reliance on multiple software vendors for various data systems.
- The need for specialized expertise in deployment and maintenance.
- Training application teams on streaming data technologies.
At the end of the day, the infrastructure and maintenance costs required by these complex technology stacks add up, putting stream processing at scale out of reach for most enterprises. As a result, enterprises that cannot fully use their streaming data miss out on the many benefits streaming data applications could offer, such as improved customer experiences, greater operational efficiencies, and a competitive edge.
Reduce your TCO with Nstream
Nstream reduces the complexity of building streaming data applications so companies can make full use of their streaming data at scale without incurring prohibitive costs or sacrificing latency. This is because Nstream reduces the need for multiple data systems to process streaming data.
Nstream does not require additional data systems such as:
- Stream processing frameworks (Pinto, Presto, etc.).
- Additional databases (Druid, Clickhouse, MongoDB).
- Additional applications servers (Spring, NodeJS, .NET).
- Data visualization software (Angular, React).
As a result, Nstream lets companies build streaming data applications and reduce TCO by lowering costs in two areas: Core infrastructure costs and ongoing operational costs.
How Nstream reduces core infrastructure costs
Nstream’s approach to streaming data application architecture reduces the need for additional data systems — and the costs that come with them (like more server and cloud computing costs).
Nstream’s stateful applications store often-requested data in localized nodes to quickly access needed information. This eliminates the need to make tons of network requests/queries back to a database, which often leads to an explosion of latency and/or costs.
For example, a traditional build for our largest customer typically requires 400 Spark nodes; with Nstream, they can achieve the same functionality with only 80 nodes.
How Nstream reduces ongoing operational costs
Eliminating the need to manage and update multiple systems frees your team’s time and resources and allows them to focus on strategic tasks like implementing use cases and driving innovation. In fact, Nstream can reduce your required engineering hours by four times by streamlining the process of designing, building, testing, and maintaining your streaming application.
With a streamlined architecture, you can focus on implementing sophisticated use cases and not on building and maintaining multiple complex open-source data systems. This can ultimately lower your infrastructure costs and architecture needs by up to 80% (as our largest customer experienced).
What’s more, Nstream’s integrated approach also reduces ongoing operational costs by eliminating the need to hire individual Subject Matter Experts (SMEs) to deploy and maintain different data systems.
Nstream offers a cost-effective and efficient way to scale as your enterprise grows. In a traditional stateless model, costs increase exponentially with data volume because continuous polling of the entire dataset is required to determine if important changes have occurred.
Nstream’s stateful approach to data streaming ensures that state — the current status of a real-world object — is locally preserved between operations and synced with related context. This eliminates the exorbitant cost of constantly scraping massive datasets in search of changes.
Use case: Nstream + marquee enterprise telecom customer
A top three global telecom company partnered with Nstream to develop streaming data applications that helped them aggregate and analyze several petabytes of streaming data from its wireless infrastructure of thousands of cell towers connecting millions of devices in the U.S.
They were accustomed to a data processing architecture in which data collection, storage, and analysis happened in different steps. As a result, they found themselves reacting to events instead of proactively flagging and addressing them.
With Nstream, they could reduce the complexity of their streaming data application architecture and build enterprise streaming data applications at scale. With its streaming data applications, the telecom can make full use of its streaming data for greater situational awareness and more proactive decision-making.
The entire process — from establishing data access to getting a production system up and running — required just six weeks. This process might take eight to ten months (or even years) with a traditional approach.
Building streaming data applications with Nstream allows you to model the real-time state of your business (whether you’re operating on cloud, on-prem, or both). Contact our team for a conversation and learn more about how a streaming data application can support your business.