You’re probably reading this on the Quix website so you might expect the comparison to conclude with “Quix is better than Flink of course”. I’ve certainly done this in the past. However, this time I wanted to provide you with a more detailed, level-headed comparison to help you make informed decisions if you’re considering Apache Flink alternatives. I’ll explain when you should consider Quix over Flink—and when Flink is the better choice.
Let’s first establish the specialties of these two technologies.
Apache Flink is a powerful, scalable open-source framework for stateful stream processing, excelling in real-time data analytics, event-driven applications, and complex transformations. It offers low-latency, high-throughput processing, fault tolerance, and advanced features like windowing, event-time processing, and state management for large-scale distributed systems.
Quix is a stream processing platform coupled with an open-source stream processing library. Quix specializes in simplifying data processing for data-intensive applications. It offers a developer environment for building, testing, and deploying streaming applications, enabling users to quickly develop data pipelines and derive insights from real-time data streams using Python or C#. In this comparison, I'll be comparing both the Quix SaaS platform and the Quix Streams library with Apache Flink.
It’s important to note early on, that target audiences for these two platforms overlap but are somewhat different. Given Flink’s complexity, different teams typically work with different aspects of Flink so it addresses multiple roles. Quix, on the other hand, is easier to use and focused on Python developers and data teams— so this comparison is written with that audience in mind
Why focus on Python developers and data teams?
Because Python is the most popular language in the Data and ML communities. These communities could benefit a lot from Flink, but there aren't yet enough education resources that appeal to their skillset.
If you're a data scientist or in a related data-centric role, you're probably more familiar with Python and Pandas than Java. However, most in-depth comparisons and analyses cater to software engineers who use Java. This is because they have historically created components that work with tools like Apache Flink and Kafka (developed in Java and/or Scala) for large organizations such as banks and automotive companies, which require robust streaming architectures.
This landscape is shifting as software and data team roles increasingly overlap. Data-driven methodologies are now widespread and constantly growing, with even startups handling gigabytes of data daily. Recruiting Java developers can be costly and time-intensive, prompting many startups to prioritize modern languages like Python. Concurrently, data professionals also contribute to software components that utilize data processing systems (such as ML models) but often face challenges due to their limited familiarity with Java ecosystem technologies.
That’s why we’re comparing Apache Flink with Quix from the perspective of Python developers, ML engineers, Data Scientists or anyone else who uses Python as their primary programming language.
But first, let’s look at the differences that are mostly language agnostic.
Difference in deployment models for Quix vs Flink
The main difference between Flink vs. Quix Streams is that Flink is a data processing framework that uses a cluster model, whereas the Quix is both an embeddable library and platform that eliminates the need for setting up clusters.
Here are those differences in more detail.
Quix and Flink have different architectural patterns
Given the different deployment models, the architectures that use each system will look decidedly different. The following diagrams are simplified abstractions that illustrate how your architecture might look when using Flink compared to Quix.
Note: anything in that is not pink indicates external systems and/or systems that you will have to take care of yourself. This will be explained in further as I walk you through the two diagrams.
Flink with Kafka as a messaging system
Apache Kafka is a popular choice as an upstream system for Flink because it enables decoupling of data sources from data processing and Kafka integrates well with Flink. However, if you do go with Kafka, you need to set up your own Kafka cluster as well as your own Flink cluster, which can take considerable time and expertise. You also need to configure your own producers to get data into Kafka which can be challenging if you’re relying purely on Python.
Quix SaaS platform with the Quix Streams Library
Because Quix is a unified platform, data sourcing, processing and analyzing are all done in one place. You don’t need to worry about any cluster setup. The clusters are hosted and configured by Quix. Infrastructural components such as Kafka and Kubernetes sit underneath a dedicated control plane which provisions and scales resources automatically. The Quix Streams library can be used as an external source to send data from Python-based data producers, or you can deploy connectors within the Quix platform to ingest data from external APIs such as websockets and IoT message hubs. Your code all runs in one place and is not distributed across multiple compute environments.
Having said that, you’re not forced to use the Quix SaaS platform. You could use your own Kafka cluster and run the Quix Streams library inside serverless functions with the cloud provider of your choice—the SaaS platform just makes things a lot easier.
What is the (Python) developer experience like in Quix vs Flink?
The answer partly depends on how much control you would like to have over putting your code into production. Given the complexities involved in managing Flink, deployment is often left to a specialist.
However, let's put that concern aside for a second and focus on how it is to write and test stream processing logic.
Firstly, Quix and Flink support slightly different sets of languages:
Apache Flink supports Java, Scala, and Python
Quix supports C# and Python.
Here, I’ll focus on Python because they both support it, and as mentioned, I want this to be a Python-centric comparison. The following table compares how the two systems fare when it comes to the developer experience for Python development.
What data processing features are supported?
Apache Flink is an extremely powerful framework that is often used for complex use cases that require stateful processing of large time windows such as processing credit card transactions for real-time fraud detection. This puts it in contrast to other stream processing libraries (such as Kafka Streams) which specialize in less computationally demanding use cases such as processing event streams for event-driven microservices. Flink can also handle batch data processing at large scales which makes it popular in batch data ecosystems where stakeholders analyze data at infrequent intervals.
In terms of its stream processing capabilities, Quix Streams lies somewhere in between Kafka Streams and Flink. It started life at McLaren to handle Formula 1 telemetry, but has evolved to handle more complex use cases.
The following table compares Quix vs Flink based on a selected set of key features:
How long does it take to put them into production?
While this comparison has focused on operational attributes, it would be remiss not to consider the time it takes to bring a stream processing solution from development to production. This depends on various factors such as your team's familiarity with the technology, the complexity of your application, and your existing infrastructure. The ease of deployment, learning curve, and overall development experience also significantly impact the time it takes to deliver a production-ready application.
In this section, I summarize how Apache Flink and Quix compare in terms of these factors while giving some very rough and general time estimates:
If you opt for a managed Flink service like Veverica, the setup and configuration time can be significantly reduced. In this case, getting up-and-running with Flink might be a matter of weeks rather than months, as you'll need to configure your application to interact with the managed service.
Although the Veverica platform reduces the complexity of Flink, it can still take a while to master Flink concepts, APIs, and stream processing features. While the learning curve for the Veverica platform may be less steep compared to self-managed Flink, it could still span anywhere from several days to a few weeks, depending on your team's existing familiarity and expertise.
When to choose Quix over Flink?
Quix is a proprietary SaaS platform that is coupled with an open-source client library, while Apache Flink is a single, open-source framework. The difference being that you can use only the Quix Streams library or use it in combination with the SaaS platform. Choose the full Quix suite when you need a managed, easy-to-use service with out-of-the-box integrations, and prefer a vendor-supported solution. Choose Flink when you require a highly customizable, open-source platform with a strong community, and are willing to manage and maintain the cluster yourself.
Flink may be better for:
- Software teams who work primarily in Java or Scala.
- Complex, large-scale stream processing tasks.
- Organizations that are willing to fund and maintain large proprietary in-house projects.
- Teams with experience in managing and maintaining Spark or Flink clusters.
- Highly custom solutions that need to integrate with other open-source tools.
Quix (SaaS platform and client library) may be better for:
- Data or Machine Learning teams who work primarily in Python.
- Complex, large-scale stream processing applications that use time-series or large data payloads.
- Teams that already use Kafka or other streaming brokers like Kinesis to transport data.
- Teams with skillsets that are weighted towards Python or C# rather than Java and Scala.
- Companies that need a quick and easy setup with less maintenance overhead.
- Use cases that align with Quix Streams' built-in features and integrations.
In conclusion, Quix and Apache Flink both offer distinct advantages for stream processing use cases, with the optimal choice hinging on your specific requirements and priorities. Quix excels at providing a managed, user-friendly platform that enables rapid time to production, making it an ideal choice for teams who prioritize a streamlined developer experience and ready-to-use integrations. In contrast, Apache Flink presents a highly customizable, feature-rich framework tailored to intricate, large-scale stream processing tasks. Although Flink may necessitate more setup and maintenance effort, its open-source nature and robust community support foster enhanced flexibility and customization.
When evaluating these solutions, weigh the trade-offs between developer experience, stream processing features, resource consumption, and time to production. In the end, selecting between Quix and Apache Flink will be guided by your team's expertise, the complexity of your use case, and your willingness to devote resources to managing and maintaining the solution.
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Mike Rosam is Co-Founder and CEO at Quix, where he works at the intersection of business and technology to pioneer the world's first streaming data development platform. He was previously Head of Innovation at McLaren Applied, where he led the data analytics product line. Mike has a degree in Mechanical Engineering and an MBA from Imperial College London.