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 #7 with an average rating of 7.9. Apache Flink holds a 14.8% mindshare in SA, compared to Apache Spark Streaming’s 3.6% mindshare. Additionally, 94% of Apache Flink users are willing to recommend the solution, compared to 94% of Apache Spark Streaming users who would recommend it.

Apache Flink

Read 18 Apache Flink reviews

3,815 Views
3,815 Comparison Views

94% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

1,291 Views
1,291 Comparison Views

94% 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: September 2025).

Buyer's Guide

Apache Flink vs. Apache Spark Streaming

September 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

7th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 14.8%, up from 10.6% compared to the previous year. The mindshare of Apache Spark Streaming is 3.6%, up from 3.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Apache Flink	14.8%
Apache Spark Streaming	3.6%
Other	81.6%

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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Himansu Jena

Sr Project Manager at Raj Subhatech

Efficient real-time data management and analysis with advanced features

There are various ways we can improve Apache Spark Streaming through best practices. The initial part requires attention to batch interval tuning, which helps small intervals in micro batches based on latency requirements and helps prevent back pressure. We can use data formats such as Parquet or ORC for storage that needs faster reads and leveraging feature predicate push-down optimizations. We can implement serialization which helps with any Kyro in terms of .NET or Java. We have boxing and unboxing serialization for XML and JSON for converting key-pair values stored in browser. We can also implement caching mechanisms for storing and recomputing multiple operations. We can use specified joins which help with smaller databases, and distributed joins can minimize users. We can implement project optimization memory for CPU efficiency, known as Tungsten. Additionally, load balancing, checkpointing, and schema evaluation are areas to consider based on performance and bottlenecks. We can use Bugzilla tools for tracking and Splunk to monitor the performance of process systems, utilization, and performance based on data frames or data sets.

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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 is user-friendly and the reporting is good."

"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."

"The documentation is very good."

"This is truly a real-time solution."

"Easy to deploy and manage."

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

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

"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."

More Apache Flink pros

"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."

"For Apache Spark Streaming, the feature I appreciated most is that it provides live data delivery; additionally, it provides the capability to send a larger amount of data in parallel."

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

"With Apache Spark Streaming's integration with Anaconda and Miniconda with Python, I interact with databases using data frames or data sets in micro versions and create solutions based on business expectations for decision-making, logistic regression, linear regression, or machine learning which provides image or voice record and graphical data for improved accuracy."

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

"It's the fastest solution on the market with low latency data on data transformations."

"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."

More Apache Spark Streaming pros

Cons

"The state maintains checkpoints and they use RocksDB or S3. They are good but sometimes the performance is affected when you use RocksDB for checkpointing."

"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."

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

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

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

"The machine learning library is not very flexible."

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

"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."

More Apache Flink cons

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."

"We would like to have the ability to do arbitrary stateful functions in Python."

"When dealing with various data types including COBOL, Excel, JSON, video, audio, and MPG files, challenges can arise with incomplete or missing values."

"The downside is when you have this the other way around in the columns, it becomes really hard to use."

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

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

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

More Apache Spark Streaming cons

Pricing and Cost Advice

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

"It's an open-source solution."

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

"It's an open source."

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

"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."

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

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

22%

Retailer

11%

Computer Software Company

11%

Manufacturing Company

Computer Software Company

24%

Financial Services Firm

21%

Healthcare Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	11

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

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?

I believe the downsides of Apache Spark Streaming are that it primarily supports structured data. Currently, in my organization, we require thousands of transcripts that need to be handled during l...

See all answers

What is your primary use case for Apache Spark Streaming?

My use cases for Apache Spark Streaming were during my academics. During that time, I used Apache Spark Streaming to transmit data live from one source to another.

See all answers

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Compared 11% 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 9% of the time

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Product Reports

Buyer's Guide

Apache Flink

September 2025

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Buyer's Guide

Apache Spark Streaming

September 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

September 2025

Free Report: Apache Flink vs. Apache Spark Streaming

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