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 #4 with an average rating of 8.0, while Apache Spark Streaming is ranked #9 with an average rating of 7.9. Apache Flink holds a 7.5% mindshare in SA, compared to Apache Spark Streaming’s 4.7% mindshare. Additionally, 95% 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 19 Apache Flink reviews

4,917 Views
4,917 Comparison Views

95% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

2,655 Views
2,655 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: June 2026).

Buyer's Guide

Apache Flink vs. Apache Spark Streaming

June 2026

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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

4th

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

9th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 7.5%, down from 14.4% compared to the previous year. The mindshare of Apache Spark Streaming is 4.7%, up from 3.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	7.5%
Apache Spark Streaming	4.7%
Other	87.8%

Streaming Analytics

Featured Reviews

Sanjay Srivastava

Software Architect at IBM

Streaming workflows have improved data integration and support real-time pipelines across platforms

We are not using Apache Flink in its advanced window capabilities. We are using the Apache Flink job in Apache SeaTunnel, meaning we can write the code inside Apache SeaTunnel. Currently, we are moving; both solutions are there. We are doing it on-premises with the help of Kubernetes and OpenShift. The main reason why Apache Flink is better is that it has more functions, and being open source with easy code in Apache SeaTunnel helps us achieve that. Cost is a major issue. I would rate the stability of the product as an eight. For Apache Flink, the final point can be rated an eight. I can recommend Apache Flink to other users for streaming support, and I am recommending it. I would rate this review an eight overall.

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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.

Read full review

Quotes from Members

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

Pros

"Among all of this, if I would talk about streaming, Apache Flink wins hands down, but there are other products like Apache Pulsar which I have no idea."

"Easy to deploy and manage."

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

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

"The documentation is very good."

"Apache Flink provides faster and low-cost investment for me; I find it to have low hardware requirements, and it's faster with low code, meaning it's easy to understand for moving the streaming data."

"With Flink, it provides out-of-the-box checkpointing and state management. It helps us in that way. When Storm used to restart, sometimes we would lose messages. With Flink, it provides guaranteed message processing, which helped us. It also helped us with maintenance or restarts."

"We value this solution's intricate system because it comes with a state inside the mechanism and product, allowing us to process batch data, stream to real-time and build pipelines, and we do not need to process data from the beginning when we pause as we can continue from the same point where we stopped, helping us save time as 95% of our pipelines will now be on Amazon and we'll save money by saving time."

More Apache Flink pros

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

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

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

"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."

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

"It is the most scalable tool that I have seen before."

"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."

"By integrating Apache Spark Streaming, the data freshness rate, and latency have significantly improved from 24-hour batch processing to less than one minute, facilitating faster communication to downstream systems, aiding marketing campaigns."

More Apache Spark Streaming pros

Cons

"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."

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

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

"I am using the Python API and I have found the solution to be underdeveloped compared to others. There needs to be better integration with notebooks to allow for more practical development."

"Flink has become a lot more stable but the machine learning library is still not very flexible."

"One way to improve Flink would be to enhance integration between different ecosystems."

More Apache Flink cons

"In terms of improvement, the UI could be better."

"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."

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

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

"The initial setup is quite complex."

"The debugging aspect could use some improvement."

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

"Integrating event-level streaming capabilities could be beneficial."

More Apache Spark Streaming cons

Pricing and Cost Advice

"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."

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

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

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

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Retailer

13%

Computer Software Company

Comms Service Provider

Financial Services Firm

18%

Comms Service Provider

Outsourcing Company

Marketing Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	12

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

Questions from the Community

What needs improvement with Apache Flink?

Apache could improve Apache Flink by providing more functionality, as they need to fully support data integration. The connectors are still very few for Apache Flink. There is a lack of functionali...

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What is your primary use case for Apache Flink?

I am working with Apache Flink, which is the tool we use for data integration. Apache Flink is for data, and we are working on the data integration project, not big data, using Apache Flink and Apa...

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What advice do you have for others considering Apache Flink?

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What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

See all answers

What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

See all answers

What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

See all answers

Comparisons

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Compared 7% of the time

WSO2 Stream Processor vs Apache Flink

Compared 6% of the time

SAP Event Stream Processor vs Apache Flink

Compared 4% of the time

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Compared 15% of the time

Confluent vs Apache Spark Streaming

Compared 10% of the time

Databricks vs Apache Spark Streaming

Compared 8% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 7% of the time

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 6% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Apache Flink

August 2026

Download Apache Flink product report

Buyer's Guide

Apache Spark Streaming

August 2026

Download Apache Spark Streaming product report

Also Known As

Flink

Spark Streaming

Overview

Apache Flink is a powerful open-source framework for stateful computations over data streams, designed for both real-time and batch processing. It efficiently handles massive volumes of data with low-latency responses, offering versatility for complex event processing scenarios.

Apache Flink excels in processing high-throughput data streams, enabling seamless state management across distributed applications. Users appreciate its robust features like stateful transformations and checkpointing, simplifying deployment in diverse environments. Though powerful, it poses challenges for beginners due to its complexity and limited documentation, requiring some prior experience to master. Its flexible integration with systems like Kafka and support for Kubernetes on AWS makes it suitable for demanding environments where quick, real-time analysis is essential.

What are the key features of Apache Flink?

Stateful Transformations: Allows complex stateful operations on data streams with precise handling.
Low Latency: Ensures real-time data processing with minimal delays.
Checkpointing: Provides efficient and reliable checkpointing for fault tolerance.
Kafka Integration: Easy integration with Kafka for seamless data ingestion and processing.
API Support: Provides robust APIs for diverse data processing needs.
Flexible Deployment: Offers options for deploying on-premise or in cloud environments.

What benefits should users look for?

Versatility: Supports both batch and stream processing in a unified model.
Community Support: Backed by an active community that continuously enhances its features.
Ease of Use: Simplifies the coding process compared to similar frameworks like Apache Storm.
Real-Time Analytics: Facilitates immediate insights and data-driven decision-making.

Organizations leverage Apache Flink primarily for real-time data processing in sectors such as retail, transportation, and telecommunications. By deploying on AWS with Kubernetes, companies can utilize it for data cleaning, generating customer insights, and providing swift real-time updates. It effectively manages millions of events per second, serving use cases like cab aggregations, map-making, and outlier detection in telecom networks, enabling seamless integration of streaming data with existing pipelines.

Apache

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

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

June 2026

Free Report: Apache Flink vs. Apache Spark Streaming

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