Apache Spark Streaming vs Azure Stream Analytics comparison

Apache and Microsoft are both solutions in the Streaming Analytics category. Apache is ranked #10 with an average rating of 8.2, while Microsoft is ranked #3 with an average rating of 7.9. Apache holds a 2.6% mindshare in SA, compared to Microsoft’s 9.2% mindshare. Additionally, 91% of Apache users are willing to recommend the solution, compared to 89% of Microsoft users who would recommend it.

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

1,347 Views
1,347 Comparison Views

91% willing to recommend

Azure Stream Analytics

Read 28 Azure Stream Analytics reviews

4,155 Views
3,781 Comparison Views

89% willing to recommend

Apache Spark Streaming

Azure Stream Analytics

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Executive SummaryUpdated on Dec 17, 2024

Azure Stream Analytics and Apache Spark Streaming are key players in the real-time analytics sector. Azure Stream Analytics seems to have the upper hand due to its seamless integration with Azure services and strong real-time capabilities.

Features: Azure Stream Analytics offers easy integration with Azure services, minimal setup requirements, and high scalability. Key features include IoT hubs, Blob storage, and data streaming to Power BI. Apache Spark Streaming supports multiple programming languages, handles large-scale data with low latency, and boasts an open-source nature for diverse project flexibility.

Room for Improvement: Azure Stream Analytics is limited by its Azure-specific nature, lacks flexibility in query customization, and has issues with Power BI integrations and pricing transparency. Apache Spark Streaming struggles with memory management and latency, lacking real-time capabilities compared to alternatives. Its setup is complex and not cloud-native, hindering cloud migrations and needing event handling and cost improvements.

Ease of Deployment and Customer Service: Azure Stream Analytics is available on public and private clouds, with solid Microsoft support, although experiences vary depending on subscription plans. Apache Spark Streaming can be deployed in various environments, relying on community forums for support, which may challenge users unfamiliar with its ecosystem.

Pricing and ROI: Azure Stream Analytics tends to be more expensive than open-source options like Apache Spark Streaming, with pricing based on streaming units causing confusion. Apache Spark Streaming's open-source nature offers an affordable alternative. Both solutions deliver decent ROI, with Azure showcasing quick deployments and Spark benefiting from its no-cost availability.

To learn more, read our detailed Apache Spark Streaming vs. Azure Stream Analytics Report (Updated: June 2025).

Buyer's Guide

Apache Spark Streaming 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 Spark Streaming

Ranking in Streaming Analytics

10th

Average Rating

8.0

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Azure Stream Analytics

Ranking in Streaming Analytics

3rd

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.6%, down from 3.7% compared to the previous year. The mindshare of Azure Stream Analytics is 9.2%, down from 12.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Oscar Estorach

Chief Data-strategist and Director at Theworkshop.es

Versatile and flexible when dealing with large-scale data streams

What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale 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. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.

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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.

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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

"As an open-source solution, using it is basically free."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."

"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."

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"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."

More Apache Spark Streaming pros

"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 most valuable aspect is the SQL option that Azure Stream Analytics provides."

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

"We find the query editor feature of this solution extremely valuable for our business."

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

"It's easy to implement and maintain pipelines with minimal complexity."

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

"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."

More Azure Stream Analytics pros

Cons

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

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

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"The solution itself could be easier to use."

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

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

"The initial setup is quite complex."

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

More Apache Spark Streaming cons

"The initial setup is complex."

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

"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."

"I would like to have a contact individual at Microsoft."

"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."

"One area that could use improvement is the handling of data validation. Currently, there is a review process, but sometimes the validation fails even before the job is executed. This results in wasted time as we have to rerun the job to identify the failure."

"There is a lack of technical support from Microsoft's local office, particularly in Taiwan."

"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

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

"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."

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

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

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

"Azure Stream Analytics is a little bit expensive."

"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."

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

"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."

"The current price is substantial."

"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."

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

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

26%

Computer Software Company

22%

Healthcare Company

University

Financial Services Firm

15%

Computer Software Company

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 Spark Streaming?

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

See all answers

What needs improvement with Apache Spark Streaming?

We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...

See all answers

What is your primary use case for Apache Spark Streaming?

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

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?

Regarding the cost of Azure Stream Analytics, I believe the price is reasonable for the tool.

See all answers

What needs improvement with Azure Stream Analytics?

There were challenges with Azure Stream Analytics. When I initially started, the learning curve was difficult because I didn't have knowledge of the service. There's setup time required to get it i...

See all answers

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Compared 24% of the time

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Compared 23% of the time

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Compared 18% of the time

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Compared 17% of the time

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Compared 3% of the time

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Product Reports

Buyer's Guide

Streaming Analytics

June 2025

Download Apache Spark Streaming product report

Buyer's Guide

Azure Stream Analytics

July 2025

Download Azure Stream Analytics product report

Also Known As

Spark Streaming

ASA

Overview

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

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

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay 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 Spark Streaming vs. Azure Stream Analytics

June 2025

Free Report: Apache Spark Streaming vs. Azure Stream Analytics

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