Apache Spark vs Azure Stream Analytics comparison

The compared Apache and Microsoft solutions aren't in the same category. Apache is ranked #1 in H , with an average rating of 8.2, and holds a 13.6% mindshare in the category. Microsoft is ranked #2 in SA , with an average rating of 6.9, and holds a 6.1% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 90% of Microsoft users who would recommend it.

Apache Spark

Read 69 Apache Spark reviews

5,582 Views
1,786 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 30 Azure Stream Analytics reviews

4,494 Views
4,134 Comparison Views

90% willing to recommend

Apache Spark

Azure Stream Analytics

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Executive SummaryUpdated on Feb 8, 2026

Apache Spark and Azure Stream Analytics are competitors in the big data processing arena. While Spark is strong in batch processing and machine learning, Azure Stream Analytics stands out due to its real-time capabilities and integration with Azure services, providing an advantage in Azure-centric environments.

Features: Apache Spark provides efficient large-scale data processing with negligible latency using frameworks like Spark Streaming, Spark SQL, and MLlib. Its in-memory computation supports fast, fault-tolerant data processing, enhancing machine learning applications. Azure Stream Analytics offers seamless integration with Azure, facilitating real-time and IoT processing. It allows SQL-like queries for streamlined analytics and offers easy integration with other Azure services, making it ideal for Azure-dependent infrastructures.

Room for Improvement: Apache Spark could enhance scalability, user-friendliness, and real-time querying integration. Better data lineage and debugging tools are also desired. Azure Stream Analytics needs improvements in flexibility, user-friendly customization, and support for complex data pipelines. Enhancements in logging, error handling, and metrics visibility would boost its effectiveness.

Ease of Deployment and Customer Service: Apache Spark is primarily deployed on-premises, relying on community support, though commercial support is available through vendors like Cloudera. Azure Stream Analytics, typically cloud-based, benefits from structured Microsoft support, providing a more seamless deployment experience.

Pricing and ROI: Apache Spark, being open-source, incurs no licensing fees unless using commercial solutions like Cloudera, but infrastructure costs can increase. It delivers high ROI through cost reduction and efficiency gains. Azure Stream Analytics charges based on data usage and streaming units, with pricing seen as fair but potentially costly at scale. Its real-time analytics capability, particularly within Azure environments, contributes to a positive ROI.

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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:

ROI

Sentiment score

5.6

Apache Spark provides up to 50% cost savings, boosting efficiency and reducing expenses significantly in machine learning analytics.

Sentiment score

4.7

Azure Stream Analytics offers quick, efficient streaming solutions with about 10% ROI, minimizing upfront costs through its cloud-based setup.

No quotes available

For more quotes and insights, download the Apache Spark report

No quotes available

For more quotes and insights, download the Azure Stream Analytics report

Customer Service

Sentiment score

6.0

Apache Spark offers vibrant community support and resources, with commercial support available through vendors like Cloudera and Hadoop.

Sentiment score

6.0

Azure Stream Analytics customer service is generally supportive, though response times and quality can vary by subscription and location.

I have received support via newsgroups or guidance on specific discussions, which is what I would expect in an open-source situation.

Devindra Weerasooriya

Data Architect at Devtech

I would rate the technical support of Apache Spark an eight because when we had questions, we found solutions, and it was straightforward.

Michael Lierheimer

Consultant, Chief Engineer, Teamleiter at infoteam Software AG

For more quotes and insights, download the Apache Spark report

There is a big communication gap due to lack of understanding of local scenarios and language barriers.

Kay Li

PU Head of Manufacturing Industry at Wiadvance Technology Co

They've managed to answer all my questions and provide help in a timely manner.

Sarath Boppudi

Data Strategist, Cloud Solutions Architect at BiTQ

The support on critical issues depends on the level of subscription that you have with Microsoft itself.

Mahmoud Abukhamseh

DevSecOps Manager at APGecommerce

For more quotes and insights, download the Azure Stream Analytics report

Scalability Issues

Sentiment score

7.4

Apache Spark's scalability and versatility enable efficient large-scale data processing, making it a reliable choice for diverse teams.

Sentiment score

7.3

Azure Stream Analytics provides efficient, scalable real-time data streaming with minimal maintenance, supporting diverse industries through straightforward scaling.

No quotes available

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Maintenance requires a couple of people, however, it's not a full-time endeavor.

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

This is crucial for applications demanding constant monitoring, such as healthcare or financial services.

Chandra Mani

Technical architect at Tech Mahindra

Azure Stream Analytics is scalable, and I would rate it seven out of ten.

Kay Li

PU Head of Manufacturing Industry at Wiadvance Technology Co

For more quotes and insights, download the Azure Stream Analytics report

Stability Issues

Sentiment score

7.4

Apache Spark is praised for its robust stability and reliability, with high user ratings despite minor configuration challenges.

Sentiment score

6.3

Azure Stream Analytics is typically stable, though challenges include VM errors and job failures; support is efficiently accessible.

Apache Spark resolves many problems in the MapReduce solution and Hadoop, such as the inability to run effective Python or machine learning algorithms.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

Without a doubt, we have had some crashes because each situation is different, and while the prototype in my environment is stable, we do not know everything at other customer sites.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

They require significant effort and fine-tuning to function effectively.

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

For example, Azure Stream Analytics processes more data every second, which is why it's recommended for real-time streaming.

Chandra Mani

Technical architect at Tech Mahindra

For more quotes and insights, download the Azure Stream Analytics report

Room For Improvement

Apache Spark needs improvements in real-time querying, user-friendliness, logging, large dataset handling, and expanded programming language support.

Azure Stream Analytics needs improved integration, flexibility, UI, job monitoring, Power BI compatibility, and AI-enhanced features for better user experience.

Various tools like Informatica, TIBCO, or Talend offer specific aspects, licensing can be costly;

Devindra Weerasooriya

Data Architect at Devtech

I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark.

Michael Lierheimer

Consultant, Chief Engineer, Teamleiter at infoteam Software AG

For more quotes and insights, download the Apache Spark report

A cost comparison between products is also not straightforward.

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

There's setup time required to get it integrated with different services such as Power BI, so it's not a straight out-of-the-box configuration.

Sarath Boppudi

Data Strategist, Cloud Solutions Architect at BiTQ

Azure Stream Analytics currently allows some degree of code writing, which could be simplified with low-code or no-code platforms to enhance performance.

Chandra Mani

Technical architect at Tech Mahindra

For more quotes and insights, download the Azure Stream Analytics report

Setup Cost

Apache Spark is cost-effective but can incur high infrastructure costs, especially in cloud setups like Databricks, with setup time variability.

Azure Stream Analytics pricing is competitive, with optimization options, but billing complexity and short free trial need improvement.

No quotes available

For more quotes and insights, download the Apache Spark report

Choosing between pay-as-you-go or enterprise models can affect pricing, and depending on data volume, charges might increase substantially.

Chandra Mani

Technical architect at Tech Mahindra

From my point of view, it should be cheaper now, considering the years since its release.

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

We sell the data analytics value and operational value to customers, focusing on productivity and efficiency from the cloud.

Kay Li

PU Head of Manufacturing Industry at Wiadvance Technology Co

For more quotes and insights, download the Azure Stream Analytics report

Valuable Features

Apache Spark provides scalable, in-memory data processing with flexible support for distributed computing, streaming, and machine learning integration.

Azure Stream Analytics provides scalable, user-friendly real-time analytics with SQL-based queries, IoT compatibility, and integrated machine learning features.

Not all solutions can make this data fast enough to be used, except for solutions such as Apache Spark Structured Streaming.

Omar Khaled

Data Engineer at a tech company with 10,001+ employees

The most important part is that everything can be connected, and the data exchange across overseas connections is fast and reliable.

Michael Lierheimer

Consultant, Chief Engineer, Teamleiter at infoteam Software AG

The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

Devindra Weerasooriya

Data Architect at Devtech

For more quotes and insights, download the Apache Spark report

It's very accurate and uses existing technologies in terms of writing queries, utilizing standard query languages such as SQL, Spark, and others to provide information.

Sarath Boppudi

Data Strategist, Cloud Solutions Architect at BiTQ

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

Chandra Mani

Technical architect at Tech Mahindra

It is quite easy for my technicians to understand, and the learning curve is not steep.

SantiagoCordero

Director, Governance & Infrastructure & Director at VASS

For more quotes and insights, download the Azure Stream Analytics report

Categories and Ranking

Apache Spark

Average Rating

8.4

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (6th), Java Frameworks (2nd)

Azure Stream Analytics

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Streaming Analytics (2nd)

Mindshare comparison

Apache Spark and Azure Stream Analytics aren’t in the same category and serve different purposes. Apache Spark is designed for Hadoop and holds a mindshare of 13.6%, down 17.6% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 6.1% mindshare, down 9.8% since last year.

Hadoop Mindshare Distribution
Product	Mindshare (%)
Apache Spark	13.6%
Cloudera Distribution for Hadoop	14.8%
HPE Data Fabric	10.5%
Other	61.1%

Hadoop

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Azure Stream Analytics	6.1%
Apache Flink	8.9%
Databricks	8.1%
Other	76.9%

Streaming Analytics

Featured Reviews

Devindra Weerasooriya

Data Architect at Devtech

Provides a consistent framework for building data integration and access solutions with reliable performance

The in-memory computation feature is certainly helpful for my processing tasks. It is helpful because while using structures that could be held in memory rather than stored during the period of computation, I go for the in-memory option, though there are limitations related to holding it in memory that need to be addressed, but I have a preference for in-memory computation. The solution is beneficial in that it provides a base-level long-held understanding of the framework that is not variant day by day, which is very helpful in my prototyping activity as an architect trying to assess Apache Spark, Great Expectations, and Vault-based solutions versus those proposed by clients like TIBCO or Informatica.

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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.

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Comms Service Provider

Manufacturing Company

Computer Software Company

Financial Services Firm

13%

Computer Software Company

10%

University

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	28
Midsize Enterprise	16
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	3
Large Enterprise	18

Questions from the Community

What is your experience regarding pricing and costs for Apache Spark?

Apache Spark is open-source, so it doesn't incur any charges.

See all answers

What needs improvement with Apache Spark?

I find that there really lacks the technical depth to do any recommendations for future updates of Apache Spark. I used it for two years for our prototype work and testing things, but because I had...

See all answers

What is your primary use case for Apache Spark?

I attempted to use Apache Spark in one of our customer projects, but after the initial test, our customer moved to another technology and another database system. I do not have any final remarks on...

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

AWS Lambda vs Apache Spark

Compared 7% of the time

Amazon EC2 vs Apache Spark

Compared 6% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 6% of the time

Spring Boot vs Apache Spark

Compared 5% of the time

Apache NiFi vs Apache Spark

Compared 5% of the time

More Apache Spark Competitors

Apache Flink vs Azure Stream Analytics

Compared 11% of the time

Databricks vs Azure Stream Analytics

Compared 10% of the time

Apache Spark Streaming vs Azure Stream Analytics

Compared 10% of the time

Amazon MSK vs Azure Stream Analytics

Compared 10% of the time

Apache NiFi vs Azure Stream Analytics

Compared 2% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Spark

May 2026

Download Apache Spark product report

Buyer's Guide

Azure Stream Analytics

May 2026

Download Azure Stream Analytics product report

Also Known As

No data available

ASA

Overview

Apache Spark is a leading open-source processing tool known for scalability and speed in managing large datasets. It supports both real-time and batch processing and is widely used for building data pipelines, machine learning applications, and analytics.

Apache Spark's strengths lie in its ability to process large data volumes efficiently through real-time and batch capabilities. With in-memory computation, it ensures fast data processing and significant performance gains. Its wide range of APIs, including those for machine learning, SQL, and analytics, make it versatile in handling complex data operations. While popular for ease of use and fault tolerance, Spark's management, debugging, and user-friendliness could benefit from improvements. Better GUIs, integration with BI tools, and enhanced monitoring are desired, alongside shuffling optimization and compatibility with more programming languages.

What are Apache Spark's key features?

Scalability: Efficiently manages large datasets across nodes.
Performance: In-memory computation for faster data processing.
Real-time Processing: Supports real-time analytics and data streaming.
APIs: Offers extensive APIs for machine learning, SQL, and analytics.

What benefits or ROI should users look for in reviews?

Ease of Use: Simplifies complex data tasks through intuitive operations.
Fault Tolerance: Ensures data reliability and continuous operations.
Integration Flexibility: Easily integrates with big data platforms and tools.

Organizations use Apache Spark predominantly for in-memory data processing, enabling seamless integration with big data frameworks. It's applied in security analytics, predictive modeling, and helps facilitate secure data transmissions in AI deployments. Industries leverage Spark's speed for sentiment analysis, data integration, and efficient ETL transformations.

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

NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions

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

Find out what your peers are saying about Apache, Cloudera, Amazon Web Services (AWS) and others in Hadoop. Updated: April 2026.

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