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.

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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

"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."

"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 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."

"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."

"Easy to deploy and manage."

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

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

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."

"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."

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

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

"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."

"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."

"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."

More Apache Spark Streaming pros

Cons

"There is a learning curve. It takes time to learn."

"There is room for improvement in the initial setup process."

"In a future release, they could improve on making the error descriptions more clear."

"Failure is another area where it is a bit rigid or not that flexible."

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

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

"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."

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

More Apache Flink cons

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

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

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

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

"The solution itself could be easier to use."

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

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

"The initial setup is quite complex."

More Apache Spark Streaming cons

Pricing and Cost Advice

"It's an open-source solution."

"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."

"It's an open source."

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

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

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

"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."

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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...

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Compared 15% of the time

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Compared 10% of the time

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Compared 8% of the time

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Compared 7% of the time

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Compared 6% of the time

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Product Reports

Buyer's Guide

Apache Flink

August 2026

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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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