Apache Flink vs Azure Stream Analytics comparison

Apache and Microsoft are both solutions in the Streaming Analytics category. Apache is ranked #6 with an average rating of 7.8, while Microsoft is ranked #3 with an average rating of 7.9. Apache holds a 13.8% mindshare in SA, compared to Microsoft’s 9.8% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 92% of Microsoft users who would recommend it.

Apache Flink

Read 17 Apache Flink reviews

5,090 Views
3,740 Comparison Views

94% willing to recommend

Azure Stream Analytics

Read 26 Azure Stream Analytics reviews

4,867 Views
4,124 Comparison Views

92% willing to recommend

Apache Flink

Azure Stream Analytics

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Azure Stream Analytics and Apache Flink compete in the stream processing category. Azure Stream Analytics has the upper hand in seamless integration with Azure services, while Apache Flink is preferred for advanced stateful processing capabilities.

Features: Azure Stream Analytics benefits from its integration with Azure infrastructure, offering seamless connectivity to IoT Hub, Blob storage, and SQL server for effective real-time analytics. Apache Flink excels in state management, providing features like checkpointing, stateful transformations, and robust event time processing for low-latency applications.

Room for Improvement: Azure Stream Analytics could enhance pricing transparency and extend integration flexibility with non-Azure tools. Its logging and data joining features need refinement. Apache Flink requires better Python support, and its complex infrastructure demands substantial expertise, making deployment and maintenance challenging.

Ease of Deployment and Customer Service: Azure Stream Analytics is known for its easy deployment on the public cloud and supportive Microsoft technical team, though increasing demands have affected the support quality. Apache Flink permits deployment across diverse environments but involves a steep learning curve despite extensive community support owing to its open-source nature.

Pricing and ROI: Azure Stream Analytics operates on a pay-as-you-go model, which can be costly for large-scale operations, though its pricing complexity is a noted issue. Apache Flink, being open-source, incurs no software cost, offering a favorable cost perspective and positive ROI when its unique strengths are leveraged.

To learn more, read our detailed Apache Flink vs. Azure Stream Analytics Report (Updated: April 2025).

Buyer's Guide

Apache Flink vs. Azure Stream Analytics

April 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

6th

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Azure Stream Analytics

Ranking in Streaming Analytics

3rd

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 13.8%, up from 9.6% compared to the previous year. The mindshare of Azure Stream Analytics is 9.8%, down from 12.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

A great solution with an intricate system and allows for batch data processing

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

Read full review

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

Native connectors and integration simplify tasks but portfolio complexity needs addressing

There are too many products in the Azure landscape, which sometimes leads to overlap between them. Microsoft continuously releases new products or solutions, which can be frustrating when determining the appropriate features from one solution over another. A cost comparison between products is also not straightforward. They should simplify their portfolio. The Microsoft licensing system is confusing and not easy to understand, and this is something they should address. In the future, I may stop using Stream Analytics and move to other solutions. I discussed Palantir earlier, which is something I want to explore in depth because it allows me to accomplish more efficiently compared to solely using Azure. Additionally, the vendors should make the solution more user-friendly, incorporating low-code and no-code features. This is something I wish to explore further.

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

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

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

"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."

"The setup was not too difficult."

"This is truly a real-time solution."

"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."

"Apache Flink offers a range of powerful configurations and experiences for development teams. Its strength lies in its development experience and capabilities."

"Easy to deploy and manage."

More Apache Flink pros

"Provides deep integration with other Azure resources."

"Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time."

"The solution has a lot of functionality that can be pushed out to companies."

"The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics."

"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex."

"It was easy for me to use from the beginning. I am accustomed to working with Microsoft."

"I like the way the UI looks, and the real-time analytics service is aligned to this. That can be helpful if I have to use this on a production service."

"The solution's most valuable feature is its ability to create a query using SQ."

More Azure Stream Analytics pros

Cons

"The machine learning library is 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."

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

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

"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."

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

"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."

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

More Apache Flink cons

"The solution offers a free trial, however, it is too short."

"The solution's interface could be simpler to understand for non-technical people."

"Easier scalability and more detailed job monitoring features would be helpful."

"The initial setup is complex."

"If something goes wrong, it's very hard to investigate what caused it and why."

"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."

"More flexibility in terms of writing queries and accommodating additional facilities would be beneficial."

"There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."

More Azure Stream Analytics cons

Pricing and Cost Advice

"It's an open-source solution."

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

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

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

"It's an open source."

"We pay approximately $500,000 a year. It's approximately $10,000 a year per license."

"The current price is substantial."

"When scaling up, the pricing for Azure Stream Analytics can get relatively high. Considering its capabilities compared to other solutions, I would rate it a seven out of ten for cost. However, we've found ways to optimize costs using tools like Databricks for specific tasks."

"The product's price is at par with the other solutions provided by the other cloud service providers in the market."

"Azure Stream Analytics is a little bit expensive."

"The cost of this solution is less than competitors such as Amazon or Google Cloud."

"The licensing for this product is payable on a 'pay as you go' basis. This means that the cost is only based on data volume, and the frequency that the solution is used."

"There are different tiers based on retention policies. There are four tiers. The pricing varies based on steaming units and tiers. The standard pricing is $10/hour."

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

24%

Computer Software Company

15%

Manufacturing Company

Retailer

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

10%

Retailer

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?

There are more libraries that are missing and also maybe more capabilities for machine learning. It could have a friendly user interface for pipeline configuration, deployment, and monitoring.

See all answers

Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

See all answers

What is your experience regarding pricing and costs for Azure Stream Analytics?

I have no problem with pricing. We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud, rather than just the infrastructure or p...

See all answers

What needs improvement with Azure Stream Analytics?

There is a lack of technical support from Microsoft's local office, particularly in Taiwan. We often have to learn online, and language can be a communication barrier since not many IT staff can sp...

See all answers

Comparisons

Spring Cloud Data Flow vs Apache Flink

Compared 25% of the time

Databricks vs Apache Flink

Compared 20% of the time

Amazon Kinesis vs Apache Flink

Compared 10% of the time

Google Cloud Dataflow vs Apache Flink

Compared 9% of the time

Amazon MSK vs Apache Flink

Compared 5% of the time

More Apache Flink Competitors

Databricks vs Azure Stream Analytics

Compared 26% of the time

Amazon MSK vs Azure Stream Analytics

Compared 24% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 19% of the time

Confluent vs Azure Stream Analytics

Compared 4% of the time

Apache Spark vs Azure Stream Analytics

Compared 3% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Flink

April 2025

Download Apache Flink product report

Buyer's Guide

Azure Stream Analytics

April 2025

Download Azure Stream Analytics product report

Also Known As

Flink

ASA

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

Azure Stream Analytics is a robust real-time analytics service that has been designed for critical business workloads. Users are able to build an end-to-end serverless streaming pipeline in minutes. Utilizing SQL, users are able to go from zero to production with a few clicks, all easily extensible with unique code and automatic machine learning abilities for the most advanced scenarios.

Azure Stream Analytics has the ability to analyze and accurately process exorbitant volumes of high-speed streaming data from numerous sources at the same time. Patterns and scenarios are quickly identified and information is gathered from various input sources, such as social media feeds, applications, clickstreams, sensors, and devices. These patterns can then be implemented to trigger actions and launch workflows, such as feeding data to a reporting tool, storing data for later use, or creating alerts. Azure Stream Analytics is also offered on Azure IoT Edge runtime, so the data can be processed on IoT devices.

Top Benefits

User friendly: Azure Stream Analytics is very straightforward and easy to use. Out of the box and with a few clicks, users are able to connect to numerous sources and sinks, and easily develop an end-to-end pipeline. Stream Analytics can easily connect to Azure IoT Hub and Azure Event Hub for streaming ingestion, in addition to connecting with Azure Blob storage for historical data ingestion.
Flexible deployment: For low-latency analytics, Azure Stream Analytics can run on Azure Stack or IoT edge. For large-scale analytics, the solution can run in the cloud. Azure Stream Analytics uses the same query language and tools for both the cloud and the edge, facilitating an easier process for developers to design exceptional hybrid architectures for streaming processes.
Cost-effective: With Azure Stream Analytics, users only pay for the streaming units they consume; there are no upfront costs. Users can easily scale up or down as needed; there is no commitment or cluster provisioning.
Trustworthy: Azure Stream Analytics guarantees event processing to be 99.99% available with a minute level of granularity. Azure Stream Analytics has embedded recovery capabilities and checkpoints to keep things running smoothly at all times. Events are never lost with Azure Stream Analytics at-least once delivery of events and exactly one event processing.

Reviews from Real Users

“Azure Stream Analytics is something that you can use to test out streaming scenarios very quickly in the general sense and it is useful for IoT scenarios. If I was to do a project with IoT and I needed a streaming solution, Azure Stream Analytics would be a top choice. The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.” - Olubisi A., Team Lead at a tech services company.

“It's used primarily for data and mining - everything from the telemetry data side of things. It's great for streaming and makes everything easy to handle. The streaming from the IoT hub and the messaging are aspects I like a lot.” - Sudhendra U., Technical Architect at Infosys

Microsoft

Sample Customers

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

Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks

Buyer's Guide

Apache Flink vs. Azure Stream Analytics

April 2025

Free Report: Apache Flink vs. Azure Stream Analytics

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