Apache Flink vs Azure Stream Analytics comparison

Why PeerSpot?

Apache and Microsoft are both solutions in the Streaming Analytics category. Apache is ranked #4 with an average rating of 8.0, while Microsoft is ranked #2 with an average rating of 6.7. Apache holds a 7.0% mindshare in SA, compared to Microsoft’s 6.2% mindshare. Additionally, 95% of Apache users are willing to recommend the solution, compared to 90% of Microsoft users who would recommend it.

Apache Flink

Read 19 Apache Flink reviews

5,102 Views
5,102 Comparison Views

95% willing to recommend

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

6,529 Views
6,072 Comparison Views

90% 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: September 2026).

Buyer's Guide

Apache Flink vs. Azure Stream Analytics

September 2026

Download the complete report

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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

Azure Stream Analytics

Ranking in Streaming Analytics

2nd

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 7.0%, down from 14.4% compared to the previous year. The mindshare of Azure Stream Analytics is 6.2%, down from 8.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Azure Stream Analytics	6.2%
Apache Flink	7.0%
Other	86.8%

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.

Read full review

Chandra Mani

Technical architect at Tech Mahindra

Has supported real-time data validation and processing across multiple use cases but can improve consumer-side integration and streamlined customization

I widely use AKS, Azure Kubernetes Service, Azure App Service, and there are APM Gateway kinds of things. I also utilize API Management and Front Door to expose any multi-region application I have, including Web Application Firewalls, and many more—around 20 to 60 services. I use Key Vault for managing secrets and monitoring Azure App Insights for tracing and monitoring. Additionally, I employ AI search for indexer purposes, processing chatbot data or any GenAI integration. I widely use OpenAI for GenAI, integrating various models with our platform. I extensively use hybrid cloud solutions to connect on-premise cloud or cloud to another network, employing public private endpoints or private link service endpoints. Azure DevOps is also on my list, and I leverage many security concepts for end-to-end design. I consider how end users access applications to data storage and secure the entire platform for authenticated users across various use cases, including B2C, B2B, or employee scenarios. I also widely design multi-tenant applications, utilizing Azure AD or Azure AD B2C for consumers. Azure Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond. They utilize Event Hubs for this purpose, as it allows for event processing. After receiving data from various sources, we validate and store it in a data store. Azure Stream Analytics can consume data from Event Hubs, applying basic validation rules to determine the validity of each record before processing.

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 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."

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

"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."

"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 setup was not too difficult."

"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

"This is a great product that works better than most of its competitors."

"Azure Stream Analytics helps with processing fast-moving data in my IoT applications because it's quicker to escalate."

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

"The support on critical issues depends on the level of subscription that you have with Microsoft itself; their support is very excellent, they understand the case immediately, they start to propose solutions and give you help, and if needed, they can work with you and you can connect with them just to explain more."

"The way it organizes data into tables and dashboards is very helpful."

"If you're not doing terribly complex scenarios, this is a quick and fast way to have your streaming pipeline set up."

"I appreciate this solution because it leverages open-source technologies. It allows us to utilize the latest streaming solutions and it's easy to develop."

"This solution is easy to use and has good fault tolerance, so I would recommend it to other organizations."

More Azure Stream Analytics pros

Cons

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

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

"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."

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

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

"Amazon's CloudFormation templates don't allow for direct deployment in the private subnet."

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

"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."

More Apache Flink cons

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

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

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

"The current price is substantial."

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

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

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

More Azure Stream Analytics 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."

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

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

"It's an open-source solution."

"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."

"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 current price is substantial."

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

"Azure Stream Analytics is a little bit expensive."

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

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Retailer

14%

Computer Software Company

Manufacturing Company

Financial Services Firm

11%

Manufacturing Company

Comms Service Provider

Computer Software Company

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	3
Large Enterprise	17

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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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?

Azure charges in various ways based on incoming and outgoing data processing activities. Choosing between pay-as-you-go or enterprise models can affect pricing, and depending on data volume, charge...

See all answers

What needs improvement with Azure Stream Analytics?

There is a need for improvement in reprocessing or validation without custom code. Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no...

See all answers

Comparisons

Spring Cloud Data Flow vs Apache Flink

Compared 8% of the time

Amazon Kinesis vs Apache Flink

Compared 7% of the time

Google Cloud Dataflow vs Apache Flink

Compared 6% of the time

WSO2 Stream Processor vs Apache Flink

Compared 6% of the time

Apache Pulsar vs Apache Flink

Compared 5% of the time

More Apache Flink Competitors

Databricks vs Azure Stream Analytics

Compared 13% of the time

Confluent vs Azure Stream Analytics

Compared 9% of the time

Apache Spark Streaming vs Azure Stream Analytics

Compared 9% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 8% of the time

Amazon MSK vs Azure Stream Analytics

Compared 7% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Flink

September 2026

Download Apache Flink product report

Buyer's Guide

Azure Stream Analytics

September 2026

Download Azure Stream Analytics product report

Also Known As

Flink

ASA

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

Azure Stream Analytics offers real-time data processing with seamless IoT hub integration and user-friendly setup. It efficiently manages data streams and supports Azure services, SQL Server, and Cosmos DB.

Azure Stream Analytics specializes in real-time data analytics, easily integrating with Microsoft technologies. It enables swift deployment, monitoring, and high-performance data streaming. Though praised for its powerful SQL language and machine learning capabilities, users face challenges with historical analysis, pricing clarity, debugging, and data connection outside Azure. Limited real-time data joining, query customization, and complex data handling are noted alongside needs for improved technical support, job monitoring, and trial periods.

What are the key features of Azure Stream Analytics?

IoT Hub Integration: Seamless connection with IoT devices for efficient data stream management.
Real-Time Analytics: Capable of processing and analyzing vast data volumes instantly.
SQL-Based Language: Powerful language for simple and flexible query creation.
Azure Service Integration: Compatible with Azure Storage, SQL Server, Cosmos DB.
Interface: User-friendly setup and swift deployment for efficient monitoring.
Machine Learning: Enhanced capabilities support predictive and preventive data analysis.

What benefits or ROI should users consider?

High-Performance Streaming: Efficient throughput for real-time data applications.
Data Partitioning: Supports scalability and flexibility in processing large data sets.
Minimal Setup Requirements: Quick and easy deployment, reducing initial setup time.
Integration Ease: Compatibility with Microsoft technologies simplifies implementation.

Azure Stream Analytics is leveraged in industries for real-time IoT data processing, predictive analytics, and accident prevention in logistics. It supports telemetry data processing for applications like predictive maintenance and integrates with Power BI for enhanced data visualization, aligning with Azure's IoT infrastructure.

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

September 2026

Free Report: Apache Flink vs. Azure Stream Analytics

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