Apache Spark Streaming vs Azure Stream Analytics comparison

Why PeerSpot?

Apache and Microsoft are both solutions in the Streaming Analytics category. Apache is ranked #8 with an average rating of 7.9, while Microsoft is ranked #2 with an average rating of 6.7. Apache holds a 4.6% mindshare in SA, compared to Microsoft’s 6.2% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 90% of Microsoft users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

2,888 Views
2,888 Comparison Views

94% willing to recommend

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

6,529 Views
6,072 Comparison Views

90% willing to recommend

Apache Spark Streaming

Azure Stream Analytics

Comparison Buyer's Guide

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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: September 2026).

Buyer's Guide

Apache Spark Streaming vs. Azure Stream Analytics

September 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 Spark Streaming

Ranking in Streaming Analytics

8th

Average Rating

7.8

Reviews Sentiment

6.4

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 Spark Streaming is 4.6%, up from 3.6% 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 Spark Streaming	4.6%
Other	89.2%

Streaming Analytics

Featured Reviews

Khoa Dang Le

Principal AI Engineer at IMT Solutions

Have faced challenges with complex data handling and seek smoother integration for machine learning workflows

I find the fault tolerance feature beneficial because I use it for serving data from a landing area. I understand all of the structures we have for Spark SQL, Spark Streaming, and MLlib. The ability of Apache Spark Streaming to handle out-of-order data using watermarking and windowing is something we use in our pipeline. Nearly 50% of our usage is based on that because we use it for landing data, and we appreciate that we can work with it. The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage. The fast storage capability is crucial because Apache Spark replaces Hadoop's MapReduce, allowing us to manage our data more efficiently.

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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

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

"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."

"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."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"The solution is better than average and some of the valuable features include efficiency and stability."

"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."

"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

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

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

"Any time I needed assistance, they were helpful."

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

"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 Stream Analytics reads from any real-time stream; it's designed for processing millions of records every millisecond."

"From the end user's perspective, Stream Analytics forms the main backbone for the entire pipeline and all the technologies."

"The most valuable features are the IoT hub and the Blob storage."

More Azure Stream Analytics pros

Cons

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

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

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"Monitoring is an area where they could definitely improve Apache Spark Streaming. When you have a streaming application, it generates numerous logs. After some time, the logs become meaningless because they're quite large and impossible to open."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

"The solution itself could be easier to use."

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

More Apache Spark Streaming cons

"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."

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

"The solution doesn't handle large data packets very efficiently, which could be improved upon."

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

"There are some improvements that could be made, first of all in pricing, because right now the pricing is a bit unclear."

"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."

"Its features for event imports and architecture could be enhanced."

"The solution could be improved by providing better graphics and including support for UI and UX testing."

More Azure Stream Analytics cons

Pricing and Cost Advice

"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."

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

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

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

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

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

"The current price is substantial."

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

"Azure Stream Analytics is a little bit expensive."

"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

15%

Outsourcing Company

13%

Comms Service Provider

Healthcare 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	9
Midsize Enterprise	2
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	3
Large Enterprise	17

Questions from the Community

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...

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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...

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?

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

Confluent vs Apache Spark Streaming

Compared 11% of the time

Databricks vs Apache Spark Streaming

Compared 9% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 7% of the time

Software AG Apama vs Apache Spark Streaming

Compared 7% of the time

Apache Flink vs Apache Spark Streaming

Compared 6% of the time

More Apache Spark Streaming Competitors

Databricks vs Azure Stream Analytics

Compared 13% of the time

Confluent vs Azure Stream Analytics

Compared 9% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 8% of the time

Apache Flink 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 Spark Streaming

September 2026

Download Apache Spark Streaming product report

Buyer's Guide

Azure Stream Analytics

September 2026

Download Azure Stream Analytics product report

Also Known As

Spark Streaming

ASA

Overview

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

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

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

September 2026

Free Report: Apache Spark Streaming vs. Azure Stream Analytics

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