5 Factors to Consider When Choosing a Stream Processing Engine

5 Factors to Consider When Choosing a Stream Processing Engine

Are you using the right stream processing engine for the job at hand?  You might think you are—and you very well might be!—but have you really examined the stream processing engines out there in a side-by-side comparison to make sure? Our Choose the Right Stream Processing Engine for Your Data Needs whitepaper makes those comparisons for you, so you can quickly and confidently determine which engine best meets your key business requirements.

Specifically, the whitepaper analyzes the technical and operational differences between modern processing engines from the Apache open source community.

We offer many of the popular open source engines used today, and that’s because we know the specific demands of your particular use case will always dictate which engine is the most optimal. And as your needs change, you need the flexibility to change engines.

While the whitepaper lays out and compares different engines, the purpose of this blog post is to define what factors the whitepaper looks at and why each one is important. There are five factors in all, and once you have a better understanding of what they are, you’ll be that much more prepared when it comes time to choose your engine.

Consideration #1: Functional aspects

Each stream processing engine comes with its own set of functional aspects. One example of a functional aspect would be the approach taken by the development communities at the engine’s inception. This centers around what the engine was designed to accomplish. Basically, each engine originated to serve a very specific purpose. The more your use case aligns with this purpose, the better suited the engine is likely to be in helping you achieve your goals.

Streaming model and time support are also functional aspects to consider. Do you need a stream processing engine with a “stateless” or “stateful” streaming model? Can the stream processing engine you’re considering distinguish between event time from processing time? Does it need to? Answering these questions will help you narrow down the field of engine candidates, which the whitepaper does here.

Consideration #2: Developmental control

Importing data from one or multiple systems to apply transformations and then export results to another system is becoming increasingly common—which means these kinds of activities must become more automated and easily repetitive. When evaluating a stream processing engine, consider its processing abstraction capabilities. Does the engine let your data engineers focus on business logic, or will it keep them too preoccupied with the execution of the processing itself?

Consideration #3: Implementation and beyond

At some point, you’ll likely need a stream processing engine that can help you move beyond the idea and development stage. One that can scale to process real-time data at a high volume. When evaluating, look at an engine’s delivery guarantee, which will tell you what to expect in terms of the latency, throughput, correctness, and fault tolerance of message delivery. 

Additionally, you should also look at the state management capabilities of any engine you consider. Does the engine provide a scheduler or full framework out-of-the box, as is the case with Kafka Streams? What about fault tolerance and resilience? Streaming architecture capabilities such as checkpointing, savepoints, redistribution, and state management are considered crucial by many organizations—but not all stream processing engines have the same levels of built-in fault tolerance.

Consideration #4: Enterprise adoption

The ease of enterprise adoption is our first big operational factor. The most effective solutions are the ones that can be adopted across your entire enterprise. This holds true for stream processing engines as well—and some are more limited in their deployment options than others.

Beyond deployment models, you should also consider an engine’s community maturity and quality of documentation. If you overlook these factors, then you risk adopting an engine with subpar developer resources that hinder productivity.

Consideration #5: Enterprise operations

Are you choosing an engine that completely and seamlessly integrates into your organization’s security framework, provides comprehensive monitoring and metrics, and can scale up and down to meet business demand? If you plan to establish a long-term solution for your data streaming needs, you’ll want an engine that can be easily integrated and managed, so that it’s ready to help you complete the tasks you have today and achieve whatever goals you have tomorrow. 

Another consideration is the scalability of an engine. Streaming workflows are usually multi-modal and unbalanced throughout the day, which makes scaling capabilities paramount. And as far as scalability goes, some engines have auto-scaling functionality while others—at the risk of sounding blunt—don’t. 

So which engine is for you?

Download the Choose the Right Stream Processing Engine for Your Data Needs whitepaper to learn which stream processing engine comes with what capabilities—and how these engines stack up against one another. 

Dinesh Chandrasekhar
Head of Product Marketing
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