20 Apr, 2023 | Explainer

Quix as an Apache Flink alternative: a side-by-side comparison

Explore the differences between Quix and Apache Flink and find out when it's better to use Quix as a Flink alternative. If you’re searching for Apache Flink alternatives, this guide offers a detailed, fair comparison to help you make an informed decision.

Words by
Mike Rosam, CEO & Co-Founder
Quix vs flink


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.

Flink is a framework that is designed to run separately from your main application or pipeline, in its own cluster or container.

The entire lifecycle of a Flink job is managed within the Flink framework which consists of primary and worker nodes.

  • A job is a discrete stream processing program that runs in its own computation environment and computation resources are a dedicated resource manager such as YARN, Mesos, or Kubernetes.

  • Flink jobs can also stop and start depending on the availability of data so that resources are released when the job is idle (more useful for batch processing).

  • When using PyFlink to write a job, you need to package your code and dependencies into a Zip file and submit it to the cluster.

Given that Flink jobs have their own deployment lifecycle, they’re usually managed by a distinct operations team. This means that developers write the stream processing logic and hand it over to a DevOps or DataOps team member to deploy

The Quix SaaS solution is fully managed platform that works in tandem with the Quix Streams library. You can embed Quix Streams in any program (written in Python or C#), so you can deploy your applications however you want: either as services within the Quix SaaS platform or as Docker containers within whatever deployment platform you use.

  • When Quix Streams is used together with the Quix SaaS platform, developers and data teams have direct control over the deployment and development lifecycle.
  • When creating and deploying a service or job in the Quix platform, developers specify their dependencies in a requirements file and Quix installs them automatically.
  • The standalone Quix Streams library can still be used in services that are hosted in a cloud provider or on-premise, but you’ll need to manage the deployment yourself.
  • When a library is embedded into an application, the CPU and memory required for stream processing are shared with the rest of the application.
  • However, the Quix SaaS platform allows you to easily separate resource consumption by running stream processing tasks as serverless functions.

In this way, you get the same separation of concerns as with Flink jobs, but developers and data teams are able to manage the deployment lifecycle end-to-end.

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.


■ Comes with comprehensive official documentation, but with fewer Python-specific examples compared to the Java/Scala API.

■ Comes with good official documentation but not yet as extensive as Flink’s.

■ Documentation is supplemented by a rich samples library as well as open source connector code for various sources and sinks (such as Azure IoT Hub and Snowflake)

API Design

■ Flink has multiple APIs with different levels of abstraction for different use cases:

■ High-level SQL queries can be executed over tables defined in the Table API.

■ The Table API is a declarative DSL centered around tables,

■ The DataStream / DataSet APIs offer the common building blocks for data processing, like transformations, windows, and so on.

■ The Stateful Stream Processing API allows users to freely process events from one or more streams, and provides consistent, fault tolerant state

■ Both the DataStream and Table APIs are supported in Python, but Python development gets very difficult the lower you go down the levels of abstraction.

■ The Quix Streams client library features a producer and consumer API with an intuitive and Pythonic functional programming model, making it easier for Python developers to adopt.

■ The producer API includes a “streaming context” feature—a simple, powerful, and performant data partition mechanism that ensures that related data is grouped together and processed on the same partition.

■ The streaming context feature serves a similar purpose to Flink’s KeyBy() function but is much easier to use and consumes far fewer resources.

■ Moreover, the SaaS platform includes websockets and an HTTP API for querying historical data streams to train machine learning models, build dashboards and export data to other systems.

■ It also features a REST API for automating tasks like creating workspaces, topics and deployments in the SaaS portal.

Tooling and Ecosystem

■ Flink has a wide range of connectors and integrations, with a large and active community.

■ However, Python developers will find many of them hard to use because they need extra configuration (E.g. you have to include a JAR file when building a Python connector)

■ This is because PyFlink-specific tooling and resources can be limited compared to the Java/Scala API.

■ Given that the Quix Streams library was only open-sourced this year (2023), Quix has not yet established a huge ecosystem.

■ However, the Quix team themselves have been developing this library for over three years and have written a wide range of open-source, pure Python connectors for various sources and sinks including Azure IoT Hub and Snowflake).

■ Quix Streams is a pure Python library (not a wrapper) which makes it easier to integrate with other tools in the wider Python ecosystem (such as machine learning frameworks, NLP libraries, data processing tools, and so on)

Monitoring and Debugging

■ Flink comes with a user interface for monitoring jobs and debugging as a monitoring API that can be hooked up to other systems such as Prometheus and Grafana.

■ It also has a queryable state store that allows developers to debug problems with stateful processing—but not easily.

For example, developers need to:
1. expose the state as queryable.
2. configure the queryable state server.
3. implement the client-side code to query the state.

■ Plus the state API is changing quickly and breaking changes are often introduced.

■ You can’t debug Flink line-by-line in your IDE because the DSL code runs server-side (raw Java abstracted away behind the scenes)

■The Quix SaaS platform includes it’s own monitoring tools such as build logs, deployment logs, and infrastructure monitoring.

■ Developers can also observe ‌live data as it flows through the topics, either as a waveform or in table format—all of these tools are accessible within the portal UI.

■ Quix does not currently support queryable state, but is on the roadmap for the near future.

■ Quix also has a monitoring API although it has been primarily intended for internal use and is not yet documented.

■ You can attach a debugger to your code in your IDE to debug Quix Streams code line-by line..


■ Flink supports local and cluster deployment, but requires additional setup and configuration compared to a library-oriented approach.

■ Local development and testing can be done with a local mini-cluster but this can be complex to run and debug.

■ Deployment typically needs to involve external infrastructure teams to deploy jobs to clusters which can slow developers down.

■ Debugging jobs that are deployed to external clusters can also be painful for the uninitiated—developers require a lot of training before they can debug effectively.

■ Python applications that use the Quix Streams library are easy to set up and deploy.

■ Developers can test with a local version of Kafka or connect to a test broker within the Quix SaaS platform.

■ Developers can use the Quix SaaS Portal user interface or the Portal API to deploy their own code.

■ The Quix Portal UI includes common, easy-to-understand, concepts such as workspaces, projects, and deployments as well as a visual tool for building data streaming pipelines.

■ Developers can develop and deploy locally or directly within the portal (using the online IDE and deployment UI).

Performance and Scalability

■ High performance and scalability, but with some overhead due to the use of Apache Beam's Python SDK for executing Python user-defined functions.

■ State is reliably stored in object storage, with each node having it’s own, yet shared state.

■ Quix offers extremely low latency data processing with nanosecond precision as well as high throughput, performance, and scalability—even when using Python.

■ Quix also uses object storage (such as Azure blob storage or S3) to store shared state, but uses Kubernetes Persistent Volumes as an abstraction layer (which makes it easier to manage and provision storage for stateful applications).

■ This makes Quix shared state management on par with Flink’s but with less management overhead.

Support and Maintenance

■ Strong support from the Apache Flink community and maintainers, with regular updates and improvements.

■ The open source Quix Streams library is maintained primarily by the Quix team, but they are currently seeking external contributors.

■ The Quix team themselves release regular updates to the library with new features added according to a consistent schedule.

Learning Curve

■ Steep learning curve for Python developers due to fewer Python-specific resources and examples.

■ PyFlink’s Domain-Specific Language (DSL) requires developers to learn a new set of commands which can add to the learning curve.

■ Gentle learning curve for Python developers due to lack of infrastructural obstacles and focus on Python as a first-class citizen.

■ No Domain-Specific Language (DSL) to learn, so developers can get started straight away.

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:

Data processing model

Flink has the strength of being a unified batch and streaming framework and is able to process both streaming and historical data.

■ It supports both bounded and unbounded streams and is agnostic in terms of the data structures and formats that it supports.

■ You can configure it to process data in one record-at-a-time or in small batches.

Quix is focused more on stream processing use cases but can be used for batch processing too.

■ It can reliably process unbounded streams as well as bounded streams.

■ You can also configure it to process data in one record-at-a-time or in small batches.

Stream Processing

■ Flink's stream processing engine supports event-time processing, which allows it to handle out-of-order events and provide accurate results even when the input data is delayed or arrives in an arbitrary order.

■ Stream processing in Flink involves advanced windowing techniques and state management capabilities to handle time-based aggregations, joins, and other complex operations on streaming data.

■ Flink can process multiple stages of a streaming job concurrently, without waiting for the previous stage to complete. This approach enables Flink to process streaming data with low latency.

■ Quix is opinionated about the incoming data structure because it is designed for time series and telemetry data.

■ The Quix Streams library allows you to define data using two primary classes: TimeSeries and EventData. You can also attach a binary blob to messages in either of these formats.

■ Like Flink, Quix can process data in record-at-a-time mode or in mini-batches which as sent as DataFrames.

■ Quix can also handle out-of-order events in a similar manner to Flink, and also supports state management to handle advanced time-based aggregations.

Batch Processing

■ Flink’s batch processing engine optimizes the execution of batch jobs by using techniques like pipelining, data partitioning, and efficient shuffling this minimizing the time it takes to complete a batch job.

■ Flink supports iterative processing for batch jobs, which is useful for machine learning and graph processing algorithms that require multiple iterations over the same dataset.

■ Flink’s batch processing engine processes one stage of the job at a time, waiting for the previous stage to complete before starting the next one—ensuring that the job is executed in a predictable and deterministic manner.

■ You can configure Flink to output but the results to any sink such as object storage systems, relational data bases, or message brokers.

■ Quix’s data serialization features and ability to handle large messages on Kafka (250Mb versus 10Mb in generic Kafka) make it useful for processing large batch-like data files in a streaming pipeline.

■ This is helpful for use cases where you need a streaming pipeline to handle a lower volume of real-time telemetry data from an autonomous vehicle and then also process a larger higher-fidelity data dump from onboard loggers at the end of any given day.

■ Quix can also close a bounded stream when all data has been consumed and automatically frees up resources when processing is no longer required.

■ Generally, users do not have to think about configuring resource allocation when running batch jobs in the Quix platform.

Windowing and time semantics

■ Flink natively supports a diverse range of functions that include standard windowing (tumbling, sliding, session, global) as well as flexible windowing based on event time, processing time, and ingestion time

■ It supports inner, outer, and interval joins and provides flexible join options. It also offers a wide range of other stateless operations such as AddColumns, IntersectAll, and FlatMap.

■ Note that these are all done with an SQL-like syntax which may pose a challenge to those who are used to working with Pandas rather than SQL.

■ Flink also has strong support for event-time processing and handling out-of-order events

■ Quix does not yet natively include any built-in transformation operations but instead relies on its tight integration with Pandas which does support many of these operations.

■ Additionally, unlike PyFlink, it is easy to incorporate external libraries with powerful data processing capabilities such as Dask, Polars, or Mars.

■ Python developers and data scientists do not have to grapple with a domain specific SQL-like syntax, and can instead write their transformations using Pandas conventions.

■ And like Flink, Quix also supports event-time processing and handling out-of-order events

Processing guarantees

■ Flink supports both “At least once” and “Exactly-once” guarantees, which makes it better equipped to ensure data consistency and integrity in the event of failures and retries.

■ Like Flink, Quix also supports both “At least once” and “Exactly-once” guarantees.

Stateful processing

■ Flink also supports a wide range of operations where storage of intermediate state is needed—such as advanced aggregations and joins.

■ Quix also supports stateful processing with stream-bounded state that uses Kubernetes PVCs. This allows developers to hold a DataFrame in memory to do stateful processing using familiar batch-oriented data processing techniques in Python and Pandas.

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:


Setup and configuration


■ Setting up and configuring a Flink cluster can be time-consuming, especially if the team has limited experience with Flink.

■ You'll need to install Flink binaries, configure cluster settings, launch the JobManager (master node) and TaskManagers (worker nodes), submit and monitor Flink jobs, ensure resource allocation, fault tolerance, and integrate data sources and sinks for stream processing.

■ This process can take several months depending on the complexity of your use case.


■ Aside from actually deploying your stream processing logic and creating pipelines, there is basically no infrastructural setup required (as long as you’re using the SaaS platform).

■ You create your workspace, configure a broker and tweak a few resource settings in just a few clicks. After that, you’re done with the setup.

Infrastructure management:


■ Maintaining a Flink cluster involves monitoring performance, troubleshooting issues, ensuring fault tolerance, scaling resources, updating Flink versions, managing job submissions, and tuning configurations for performance optimization, while adhering to best practices to ensure the smooth operation of the cluster.

■ This responsibility can occupy one employee full time.


Again, with the Quix SaaS platform, there is very little infrastructure and platform management required.

■ You may have to tweak your replication settings or resource settings when deploying services, but it’s comparable to managing Lambda functions in AWS (except without the cold starts).

■ All of the complexity involved in managing Kafka and Kubernetes is abstracted away under the Quix control plane.

Learning curve:


■ The learning curve associated with Apache Flink is famously difficult, so it can take some time for teams to familiarize themselves with the technology.

■ Depending on the team's prior experience, this can take anywhere from a few weeks to a few months.


■ While the learning curve for Quix is dramatically shorter than for Flink, teams still need some time to familiarize themselves with the service and how it integrates with other external services.

■ Quix also has some unique concepts that aren’t yet covered in external forums like StackOverflow. The main reference for developers will be the Quix educational material and Slack community.

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.


Try Quix for yourself for free (no credit card, no time limit).

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words by
Mike Rosam, CEO & Co-Founder

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.

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