Top 7 Apache Flink Alternatives: A Deep Dive
Join us in exploring the top 7 Apache Flink alternatives: Spark Structured Streaming, ksqlDB, RisingWave, Arroyo, Materialize, Quix, and Bytewax, and get a detailed comparison of them.
Join us in exploring the top 7 Apache Flink alternatives: Spark Structured Streaming, ksqlDB, RisingWave, Arroyo, Materialize, Quix, and Bytewax, and get a detailed comparison of them.
Apache Flink is a powerful and popular open-source stream processing framework that has gained significant traction in the world of big data analytics and real-time data processing. It provides a scalable, fault-tolerant, and highly efficient platform for processing data streams. However, like any other technology, it's essential to explore alternatives to ensure you're making the right choice for your specific use case. In this article, we will take a deep dive into the top seven Apache Flink alternatives, each offering its unique strengths and capabilities.
Spark Structured Streaming is a scalable and fault-tolerant stream processing engine built on top of the Apache Spark framework. It allows you to process and analyze real-time data streams using the same APIs and programming constructs as batch processing in Spark. Structured Streaming provides a high-level, declarative API for processing data streams, making it easier for developers to work with streaming data.
With the vast growth of Spark Structured Streaming, Databricks, the tech unicorn behind Apache Spark and Spark Structured Streaming, announced Project LightSpeed in 2022. Project Lightspeed is an umbrella project aimed at improving a couple of key aspects of Spark Streaming:
Project Lightspeed is a significant undertaking, but it has the potential to make Spark Structured Streaming a more powerful and versatile stream processing engine. I am excited to see how it develops in the future.
KsqlDB is a stream processing engine built on top of Apache Kafka and Kafka Streams. It combines powerful stream processing with a relational database model using SQL syntax. This makes it a powerful tool for building real-time applications that need to process and analyze streaming data. Some of the key features of ksqlDB include:
KsqlDB can be deployed on a variety of platforms, including Confluent Cloud. When ksqlDB is deployed on Confluent Cloud, it is managed by Confluent and is automatically provisioned, scaled, and updated. This makes it easy to get started with ksqlDB and to focus on building applications rather than managing infrastructure.
RisingWave is an open-source distributed SQL streaming database designed for the cloud. It is designed to reduce the complexity and cost of building real-time applications. RisingWave consumes streaming data, performs incremental computations when new data comes in, and updates results dynamically. As a database system, RisingWave maintains results in its own storage so that users can access data efficiently.
Some of the key features of RisingWave:
RisingWave is fully open-sourced so that you can easily deploy.
Arroyo is an open source distributed stream processing engine written in Rust. It is designed to efficiently perform stateful computations on streams of data. Arroyo lets you ask complex questions of high-volume real-time data with sub-second results.
The Arroyo project was started by a team of engineers from YC W23. They are passionate about making real-time data processing more accessible and affordable. They believe that Arroyo can help organizations of all sizes to take advantage of the power of real-time data.
The Arroyo project is still under development, but it has already been used by a number of organizations, including Plaid, Affirm, and Stitch Fix. The project is open source, so anyone can contribute to its development.
Here are some of the features of the Arroyo project:
Arroyo can be self-hosted, or used via the Arroyo Cloud service managed by Arroyo Systems. If you are looking for a powerful and efficient stream processing engine, then Arroyo is a good option to consider. It is still under development, but it has a lot of potential.
Materialize is a streaming database that allows you to process data at speeds and scales not possible in traditional databases, but without the cost, complexity, or development time of most streaming engines. It is a good fit for applications that need to process data in real time, such as fraud detection, anomaly detection, and real-time analytics.
Materialize combines the accessibility of SQL databases with a streaming engine that is horizontally scalable, highly available, and strongly consistent. In particular, it is strong in the following aspects:
Materialize is a new kind of data warehouse built for operational workloads: the instant your data changes, Materialize reacts.
Quix Platform is a complete system that enables you to develop, debug, and deploy real-time streaming data applications. Quix provides an online IDE and an open-source stream processing library called Quix Streams. Quix Streams is a client library that can be used in Python or C# code to develop custom elements of a processing pipeline.
Quix Platform was built on top of a message broker, specifically Kafka, rather than on top of a database, as databases introduce latency that can result in problems in real-time applications, and can also present scaling issues. Quix Platform helps abstract these issues, providing you with a scaleable and cost-effective solution.
From the top-down, the Quix stack provides the following:
Bytewax is an open source Python framework for building highly scalable dataflows in a streaming or batch context. It is based on the Timely Dataflow library, which is a dataflow processing library written in Rust. Bytewax provides a number of features that make it a powerful tool for building stream processing applications, including:
Bytewax is a relatively new framework, but it has a lot of potential. It is a good choice for organizations that are looking for a powerful and flexible stream processing framework that is written in Python.
There are many factors to consider when comparing stream processing frameworks, especially making decisions for adapting to the production. Here are some of the most important ones:
Conclusion
Apache Flink is undoubtedly a strong and powerful stream processing framework, but it’s essential to explore alternatives to determine the best fit for your specific use case. The seven alternatives discussed in this article offer a range of features and integrations, making them valuable contenders for various real-time data processing needs. When choosing an alternative to Apache Flink, consider factors such as your existing technology stack, scalability requirements, and the complexity of your stream processing tasks. Ultimately, the right choice will depend on your organization’s unique circumstances and goals.
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