Feature image Apache Flink vs. Spark

Apache Flink vs. Spark:
A Comprehensive Comparison

By Laurent Mauer · November 3, 2022 · 6 min read

In this article, we’ll compare two of the most popular big data processing frameworks, Apache Flink and Apache Spark.

We’ll go over the key differences between the two, as well as when to use each one.

Let’s dive into those two frameworks in detail with their benefits, key differences and use cases.

Introduction to Apache Flink

Apache Flink is an open source framework for efficient, distributed stream and batch data processing.

Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams.

Flink’s streaming dataflow model enables the construction of arbitrarily complex data processing topologies with a high degree of optimization.

This makes it possible to process very large data sets with unprecedented speed and efficiency.

The following sections provide an overview of the key features of Apache Flink:

  • Efficient execution of streaming and batch programs
  • Support for event-time processing
  • Stateful stream processing
  • Fault-tolerance
  • Programming APIs in Java and Scala
  • Rich set of connectors for popular data sources and sinks
  • Integration with Apache Hadoop YARN

Introduction to Apache Spark

Apache Spark is a fast and general-purpose cluster computing system.

It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.

Spark excels at iterative and interactive processing, and through the use of RDDs (Resilient Distributed Datasets), it efficiently supports iterative algorithms.

Spark also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

The Key Differences between Flink and Spark

There are several key differences between Apache Flink and Apache Spark: 

  • Flink is designed specifically for stream processing, while Spark is designed for both stream and batch processing.
  • Flink uses a streaming dataflow model that allows for more optimization than Spark’s DAG (directed acyclic graph) model.
  • Flink supports exactly-once processing semantics, while Spark only supports at-least-once processing semantics.

One key difference between Flink and Spark is that Flink is designed specifically for stream processing, while Spark is designed for both stream and batch processing

This means that Flink is able to optimize its processing for streaming data, while Spark has to process streaming and batch data in the same way. This may lead to some inefficiencies in Spark’s processing.

Flink can guarantee that data is processed exactly once, while Spark can only guarantee that data is processed at least once. This can be important for ensuring the accuracy of data processing.

When to Use Apache Flink

There are several situations in which Apache Flink is the best choice for data processing: 

  • If your data is streaming in real-time, then Flink is the obvious choice.
  • If you require very high throughput and low latency, then Flink is again a good choice. 
  • If you need exactly once processing semantics, then you’ll need to use Flink.

In addition to the above three situations, there are a few other cases where Flink is a good option.

If you have a lot of data that needs to be processed in parallel, then Flink can be a good solution.

Additionally, if you need to perform complex data processing, such as machine learning or graph processing, then Flink can be a good option.

Overall, Flink is a good choice for data processing in a variety of situations.

If you have streaming data, high throughput requirements, low latency requirements, or complex data processing needs, then Flink is worth considering.

When to Use Spark

There are also several situations in which Apache Spark is the best choice: 

  • If you’re processing both streaming and batch data, then Spark is a good choice.
  • If you don’t need exactly-once processing semantics, then Spark may be a better choice due to its lower complexity.
  • If you’re processing data that’s not streaming in real-time, then Spark may again be a better choice.

Another situation where Spark may be the best choice is if you’re processing data that doesn’t fit well into the Hadoop ecosystem.

For example, if you’re processing data that’s not in the HDFS format, then Spark may be a better option.


In this article, we’ve compared two of the most popular big data processing frameworks, Apache Flink and Apache Spark.

We’ve gone over the key differences between the two, as well as when to use each one.

In general, Flink is the best choice for stream processing, while Spark is the best choice for batch processing.

Both Flink and Spark are powerful tools that can help you process big data.

It’s important to choose the right tool for the job, and to understand the key differences between the two.

With this knowledge, you can make the best decision for your specific needs.

We hope you have found this guide helpful and that you will start using analytical functions in your own SQL queries or you can train yourself here with sample datasets.

At RestApp, we’re building a Data Activation Platform for modern data teams with our large built-in library of connectors to databases, including MongoDB, data warehouses and business apps.

We have designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or try it for free with a sample dataset.

Discover the next-gen end-to-end data pipeline platform with our built-in No Code SQL, Python and NoSQL functions. Data modeling has never been easier and safer thanks to the No Code revolution, so you can simply create your data pipelines with drag-and-drop functions and stop wasting your time by coding what can now be done in minutes! 

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Laurent Mauer
Laurent Mauer
Laurent is the head of engineer at RestApp. He is a multi-disciplinary engineer with experience across many industries, technologies and responsibilities. Laurent is at the heart of our data platform.

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