Apache Flink vs Apache Spark Streaming comparison

Apache Flink and Apache Spark Streaming are both solutions in the Streaming Analytics category. Apache Flink is ranked #5 with an average rating of 7.8, while Apache Spark Streaming is ranked #11 with an average rating of 8.3. Apache Flink holds a 14.5% mindshare in SA, compared to Apache Spark Streaming’s 3.1% mindshare. Additionally, 94% of Apache Flink users are willing to recommend the solution, compared to 92% of Apache Spark Streaming users who would recommend it.

Apache Flink

Read 18 Apache Flink reviews

3,972 Views
3,972 Comparison Views

94% willing to recommend

Apache Spark Streaming

Read 12 Apache Spark Streaming reviews

1,349 Views
1,349 Comparison Views

92% willing to recommend

Apache Flink

Apache Spark Streaming

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Apache Flink and Apache Spark Streaming are competing in the stream processing category. Flink has the upper hand in real-time analytics with its low-latency performance and complex event processing features, whereas Spark Streaming excels in versatility and ecosystem support, easing the integration process with existing infrastructures.

Features: Apache Flink offers real-time data processing with stateful transformations, support for both streaming and batch data, and a checkpointing mechanism for fault tolerance. Spark Streaming provides robust integration with the Spark ecosystem, a unified API for batch and streaming, and excellent scalability for large data processing tasks.

Room for Improvement: Apache Flink can benefit from improved ease of deployment and more comprehensive community support. Its complexity may require a steeper learning curve and additional resource allocation for optimal performance. Spark Streaming could improve in handling extremely low latency tasks and offering advanced stateful stream processing features. Its dependency on existing Spark infrastructure might limit standalone deployments, and additional support for complex event processing could be valuable.

Ease of Deployment and Customer Service: Apache Flink offers flexibility in deployment but can be complex, often requiring in-depth expertise. Its community-driven support might pose challenges for less technical users. Apache Spark Streaming seamlessly integrates within Spark infrastructures, simplifying deployment. With robust community and commercial support options, it offers more accessible user resources.

Pricing and ROI: Apache Flink may involve higher initial costs because of its complexity but can deliver significant ROI with its low-latency applications. Apache Spark Streaming stands out in cost-efficiency due to its smooth integration capabilities, potentially yielding quicker ROI through reduced implementation time and leveraging existing resources.

To learn more, read our detailed Apache Flink vs. Apache Spark Streaming Report (Updated: July 2025).

Buyer's Guide

Apache Flink vs. Apache Spark Streaming

July 2025

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Apache Flink

Ranking in Streaming Analytics

5th

Average Rating

7.8

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

11th

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 14.5%, up from 9.9% compared to the previous year. The mindshare of Apache Spark Streaming is 3.1%, down from 3.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Aswini Atibudhi

Distinguished AI Leader at Walmart Global Tech at Walmart

Enables robust real-time data processing but documentation needs refinement

Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing. It's essential to have a clear foundation; hence, it can be tough for beginners. However, once they grasp the concepts and have examples or references, it becomes easier. Intermediate users who are integrating with Kafka or other sources may find it smoother. After setting up and understanding the concepts, it becomes quite stable and scalable, allowing for customization of jobs. Every software, including Apache Flink, has room for improvement as it evolves. One key area for enhancement is user-friendliness and the developer experience; improving documentation and API specifications is essential, as they can currently be verbose and complex. Debugging and local testing pose challenges for newcomers, particularly when learning about concepts such as time semantics and state handling. Although the APIs exist, they aren't intuitive enough. We also need to simplify operational procedures, such as developing tools and tuning Flink clusters, as these processes can be quite complex. Additionally, implementing one-click rollback for failures and improving state management during dynamic scaling while retaining the last states is vital, as the current large states pose scaling challenges.

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Venkata Phaneendra Reddy Janga

Data Engineer III at Verizon

Improved data latency and integration with diverse data sources enables robust real-time processing

The best feature of Apache Spark Streaming is that it's built upon the Spark SQL engine. This is easy for someone coming from a SQL background to work with real-time data, even if they are new to real-time processing. They can quickly get started using the Spark SQL engine. Another valuable feature is that we can control many aspects such as the configuration of the engine, memory management, and have a checkpointing mechanism that allows us to manually start or restart jobs from a specific point. This is particularly useful for restoring messages of a Kafka topic from a specific date and time using the checkpointing mechanism. The integration with Spark's ecosystems such as MLlib and GraphX has significant potential, although I have not worked on that part as we focus mainly on data engineering. We can handle late-arriving data with Apache Spark Streaming. Sometimes aggregation results might be missed if data arrives out of order, but features such as windowing allow us to manage out-of-order data by specifying a watermark time. Recently released mechanisms to query the state make it easier to handle data programmatically.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."

"The documentation is very good."

"Apache Flink's best feature is its data streaming tool."

"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."

"The ease of usage, even for complex tasks, stands out."

"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."

"The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."

"It is user-friendly and the reporting is good."

More Apache Flink pros

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"As an open-source solution, using it is basically free."

"The solution is better than average and some of the valuable features include efficiency and stability."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."

"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

More Apache Spark Streaming pros

Cons

"Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing."

"Apache Flink should improve its data capability and data migration."

"Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool."

"The machine learning library is not very flexible."

"In terms of improvement, there should be better reporting. You can integrate with reporting solutions but Flink doesn't offer it themselves."

"There are more libraries that are missing and also maybe more capabilities for machine learning."

"One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."

"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."

More Apache Flink cons

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"It was resource-intensive, even for small-scale applications."

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

"We don't have enough experience to be judgmental about its flaws."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

"The initial setup is quite complex."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

More Apache Spark Streaming cons

Pricing and Cost Advice

"This is an open-source platform that can be used free of charge."

"It's an open-source solution."

"It's an open source."

"Apache Flink is open source so we pay no licensing for the use of the software."

"The solution is open-source, which is free."

"Spark is an affordable solution, especially considering its open-source nature."

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"People pay for Apache Spark Streaming as a service."

"I was using the open-source community version, which was self-hosted."

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

12%

Retailer

Manufacturing Company

Computer Software Company

22%

Financial Services Firm

21%

University

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Apache Flink?

The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. ...

See all answers

What is your experience regarding pricing and costs for Apache Flink?

The solution is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.

See all answers

What needs improvement with Apache Flink?

Apache should provide more examples and sample code related to streaming to help me better adapt and utilize the tool. There is a need for increased awareness and education, especially around best ...

See all answers

What do you like most about Apache Spark Streaming?

Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.

See all answers

What needs improvement with Apache Spark Streaming?

We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...

See all answers

What is your primary use case for Apache Spark Streaming?

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

See all answers

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Compared 2% of the time

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Compared 14% of the time

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Compared 10% of the time

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Compared 9% of the time

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Compared 9% of the time

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Compared 8% of the time

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Product Reports

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Apache Flink

August 2025

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August 2025

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Also Known As

Flink

Spark Streaming

Overview

Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.

Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.

Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.

Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.

Apache Flink Features

Distributed execution of streaming programs on clusters of computers
Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
Streaming SQL query engine with support for windowing functions
Low latency query execution in milliseconds
Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
Powerful API that supports both batch and streaming applications
Runs on clusters of commodity hardware with minimal configuration
Can be integrated with other technologies, such as Apache Spark for complex data mining

Apache Flink Benefits

Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.

Fault tolerance: Flink can automatically detect and recover from failures in the system.

Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.

Reviews from Real Users

Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:

The head of data and analytics at a computer software company notes, “The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis.”

Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”

Apache

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

Apache

Sample Customers

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

Buyer's Guide

Apache Flink vs. Apache Spark Streaming

July 2025

Free Report: Apache Flink vs. Apache Spark Streaming

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