Apache Flink vs Azure Stream Analytics comparison

Apache and Microsoft are both solutions in the Streaming Analytics category. Apache is ranked #5 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 9.4% 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 18 Apache Flink reviews

4,379 Views
1,954 Comparison Views

94% willing to recommend

Azure Stream Analytics

Read 27 Azure Stream Analytics reviews

4,141 Views
2,556 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: June 2025).

Buyer's Guide

Apache Flink vs. Azure Stream Analytics

June 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

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 June 2025, in the Streaming Analytics category, the mindshare of Apache Flink is 13.8%, up from 9.7% compared to the previous year. The mindshare of Azure Stream Analytics is 9.4%, down from 12.5% 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 ( /products/every-reviews ) 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 ( /categories/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.

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

"The setup was not too difficult."

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

"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."

"Allows us to process batch data, stream to real-time and build pipelines."

"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."

"What I appreciate best about Apache Flink is that it's open source and geared towards a distributed stream processing framework."

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

"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."

More Apache Flink pros

"Technical support is pretty helpful."

"Cloud tools and cloud services enable flexibility and lower entry barriers for Taiwanese enterprises."

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

"I like the IoT part. We have mostly used Azure Stream Analytics services for it"

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

"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."

"The life cycle, report management and crash management features are great."

More Azure Stream Analytics 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."

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

"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."

"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."

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

"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."

"Apache Flink's documentation should be available in more languages."

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

More Apache Flink cons

"There are too many products in the Azure landscape, which sometimes leads to overlap between them."

"It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."

"The collection and analysis of historical data could be better."

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

"Azure Stream Analytics could improve by having clearer metrics as to the scale, more metrics around the data set size that is flowing through it, and performance tuning recommendations."

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

"The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required."

"The solution’s customer support could be improved."

More Azure Stream Analytics cons

Pricing and Cost Advice

"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."

"It's an open-source solution."

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

"The current price is substantial."

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

"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."

"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 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."

"I rate the price of Azure Stream Analytics a four out of five."

"Azure Stream Analytics is a little bit expensive."

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

14%

Manufacturing Company

Retailer

Computer Software Company

15%

Financial Services Firm

15%

Manufacturing Company

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?

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

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 24% of the time

Databricks vs Apache Flink

Compared 20% of the time

Amazon Kinesis vs Apache Flink

Compared 11% of the time

Google Cloud Dataflow vs Apache Flink

Compared 8% of the time

Amazon MSK vs Apache Flink

Compared 5% of the time

More Apache Flink Competitors

Databricks vs Azure Stream Analytics

Compared 24% of the time

Amazon MSK vs Azure Stream Analytics

Compared 24% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 18% of the time

Confluent vs Azure Stream Analytics

Compared 5% 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

June 2025

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Buyer's Guide

Azure Stream Analytics

June 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

June 2025

Free Report: Apache Flink vs. Azure Stream Analytics

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