Data Streaming Technologies: Which is Right for My Business?
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.
When selecting data streaming technologies to manage their streaming data, companies have several existing and well-known options depending on their specific business and technical needs.
However, companies looking to make the best use of their streaming data should consider augmenting their tech stack with streaming applications, which fill the application layer gap and promise to unlock more sophisticated business use cases.
Popular data streaming technologies
Data streaming technologies compute data as it is produced or received. Most stream processors utilize a “store-then-process” architecture, in which queries are made once the data is no longer in motion.
The table below details the differences in capabilities and strengths of some of the most popular data streaming technologies:
|A distributed streaming platform that allows you to publish and subscribe to streams of records in real-time.
|* Distributed log
- Message queue
- Stream processing | * Scalability
- High-throughput | | Apache Pulsar | A distributed streaming platform that allows you to publish and subscribe to streams of messaging in real-time. | * Stream Processing
- Message Queue | * Fault-tolerance
- Scalability | | Amazon Kinesis | A fully-managed streaming data service that allows you to collect, process, and analyze real-time, streaming data from various sources. | * Distributed Log
- Message Queue
- Stream Processing | * Scalability
- Integrates well with other AWS services | | Azure Stream Analytics | A fully-managed real-time data stream processing service provided by Microsoft Azure. | * Stream Processing
- Real-time Analytics | * Integration with Azure services
- Low latency
- Easy to use
- Scalable | | Spark Streaming | A scalable, fault-tolerant, stream processing engine built on Apache Spark. | * Stream Processing
- Real-time Analytics | * Integration with Apache Spark
- Easy to use
- Real-time processing
- High throughput
- Fault tolerance | | Flink | A distributed stream processing framework for high-throughput, low-latency data streaming applications. | * Stream Processing
- Real-time Analytics | * High performance
- Low latency
- Fault tolerance
- Support for batch processing | | ksqlDB | An event streaming database built on top of Apache Kafka that enables real-time data processing using SQL-like queries. | * Stream Processing
- Query Language | * Easy to use
- Real-time processing
- Integration with Apache Kafka
- Support for stateful processing |
Where Nstream falls in the current streaming data technology ecosystem
Nstream Cloud is the fastest way to build full-stack applications that enable real-time observability, live modeling of complex business operations, and responsive decision automation at scale. Entire applications can run at the speed of their fastest data sources, and real-time insights are delivered and can be acted on instantly.
With Nstream, developers can execute technical use cases, such as the following:
- Real-time streaming analytics: Analyze incoming data as it arrives while incorporating context from static data sources.
- Real-time decision automation: Use contextual data to act quickly on emerging trends.
- Real-time visibility into business entities: Many companies can provide a solid overview of inventory but cannot pare down into tracking individual assets. With streaming applications, you can optimize your architecture to track individual assets.
How to strengthen your tech stack with Nstream
Real-time streaming applications built with Nstream can expand the standard capabilities of stream processors and analytics, which continually execute stateful business logic instead of responding to queries. As a result, Nstream can be used to enhance the data streaming technology you currently use, including:
- Kafka: Nstream makes it possible for those using Kafka to obtain full use of their real-time data. Developers can create streaming applications allowing businesses to apply context to their data and act on it in real time.
- Pulsar: Pulsar services provide an event broker, which can be connected to Nstream. That connection allows businesses to execute business logic using data from multiple topics in real time.
- Flink: While Flink can be used for some real-time processing, the context in which each event is interpreted is related to the event (and its type) only. By pairing Flink with Nstream, Web Agents can compute at any time on new data, previous state, and the states of all other agents to which they are linked.
To see how you can easily integrate Nstream with your current streaming tech, watch our video detailing how Nstream’s SwimOS works in tandem with technologies Kafka and Flink.
Choosing the best data streaming technologies for your business
Organizations must incorporate different data streaming technologies depending on the desired use case to yield the best result. Using the following steps, you can best guide the search for streaming technologies that best fit your organization:
- Identify use cases: First, identify the use cases that would benefit most from streaming technologies. Common use cases include real-time recommendations, asset tracking, anomaly/fraud detection, predictive maintenance, or personalized offers. Also document specific business requirements, data sources, and expected outcomes.
- Evaluate requirements: Next, evaluate the specific requirements for each use case. This includes understanding the expected data volume, velocity, and variety, as well as the expected latency and throughput.
- Research available streaming technologies: Given the wide field, research each technology to understand its capabilities, limitations, and suitability. Compare each technology to the specific use cases you plan on using it for.
- Conduct proof of concepts: Once you have shortlisted a few streaming technologies, conduct proof of concepts to validate their suitability for use cases.
- Evaluate operational aspects: Apart from the technical capabilities of the streaming technologies, it’s important to evaluate the operational aspects, such as ease of deployment, management, and maintenance. Consider the skills and resources required to operate and maintain the selected streaming technology.
- Evaluate total cost of ownership: Finally, evaluate the overall costs associated with the selected streaming technology, including software vendor licensing costs, infrastructure, networking, and, most importantly, the operational costs of managing the deployment by your technical team. Choose the technology that provides the best value for money while meeting the specific use case requirements.
Nstream provides real-time data streaming at a large scale
Streaming apps present an exciting opportunity to take existing streaming technologies to the next level. While several options are suited for different use cases, Nstream is the first open-source, full-stack streaming data application platform.
Want to learn more about the Nstream platform and how it can work with your tech stack? Read more on our platform and approach here.