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 8.6, and holds a 17.8% mindshare in the category. Microsoft is ranked #3 in SA , with an average rating of 7.9, and holds a 9.8% mindshare. Additionally, 90% of Apache users are willing to recommend the solution, compared to 92% of Microsoft users who would recommend it.

Apache Spark

Read 66 Apache Spark reviews

1,404 Views
1,090 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 26 Azure Stream Analytics reviews

4,867 Views
4,124 Comparison Views

92% willing to recommend

Apache Spark

Azure Stream Analytics

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Executive SummaryUpdated on Apr 20, 2025

Apache Spark and Azure Stream Analytics are key competitors in the data processing and real-time analytics market. Azure Stream Analytics stands out for real-time analytics and ease of integration, making it the preferred choice for those prioritizing seamless cloud integration.

Features: Apache Spark offers in-memory computing, Spark Streaming for real-time processing, and MLlib for machine learning, enabling low-latency and large-scale data processing. Azure Stream Analytics excels in real-time data analytics through SQL-based queries, easy integration with Azure services, and simplifies the management of data streams for users.

Room for Improvement: Apache Spark could enhance its scalability and stability, real-time query capabilities, and improve integration with BI tools. Documentation improvements and setup simplification would aid user optimization. Meanwhile, Azure Stream Analytics could benefit from more flexible query customization, improved integration beyond the Azure ecosystem, and enhanced user-friendliness and performance metrics.

Ease of Deployment and Customer Service: Apache Spark provides flexible deployment across different environments, though support often depends on community and third-party services. In contrast, Azure Stream Analytics offers robust integration within Azure, backed by Microsoft's consistent support and guidance, ideal for public cloud deployment.

Pricing and ROI: Apache Spark's open-source framework lowers licensing costs but may incur infrastructure expenses, offering high ROI for those with in-house expertise in optimizing resources. Azure Stream Analytics operates on a 'pay as you go' model, appealing to small-scale applications but with potential cost increases as usage expands. The product is attractive for users preferring cloud-integrated solutions with straightforward costs.

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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

Average Rating

8.4

Reviews Sentiment

7.7

Number of Reviews

Ranking in other categories

Hadoop (1st), Compute Service (4th), Java Frameworks (2nd)

Azure Stream Analytics

Average Rating

8.0

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Streaming Analytics (3rd)

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 17.8%, down 21.4% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 9.8% mindshare, down 12.6% since last year.

Hadoop

Streaming Analytics

Featured Reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Reliable, able to expand, and handle large amounts of data well

We use batch processing. It works well with our formats and file versions. There's a lot of functionality. In our pipeline each hour, we make a copy of data from MongoDB, of the changes from MongoDB to some specific file. Each time pipeline copied all of the data, it would do it each time without changes to all of the tables. Tables have a lot of data, and in the last MongoDB version, there is a possibility to read only changed data. This reduced the cost and configuration of the cluster, and we saved about $150,000. The solution is scalable. It's a stable product.

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 memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly."

"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."

"The good performance. The nice graphical management console. The long list of ML algorithms."

"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."

"We use Spark to process data from different data sources."

"I appreciate everything about the solution, not just one or two specific features. The solution is highly stable. I rate it a perfect ten. The solution is highly scalable. I rate it a perfect ten. The initial setup was straightforward. I recommend using the solution. Overall, I rate the solution a perfect ten."

"The product's initial setup phase was easy."

"The most valuable feature of Apache Spark is its ease of use."

More Apache Spark pros

"It's scalable as a cloud product."

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

"We use Azure Stream Analytics for simulation and internal activities."

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

"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."

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

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

"It's a product that can scale."

More Azure Stream Analytics pros

Cons

"The main concern is the overhead of Java when distributed processing is not necessary."

"It needs a new interface and a better way to get some data. In terms of writing our scripts, some processes could be faster."

"It's not easy to install."

"Apache Spark lacks geospatial data."

"Stability in terms of API (things were difficult, when transitioning from RDD to DataFrames, then to DataSet)."

"The initial setup was not easy."

"The product could improve the user interface and make it easier for new users."

"Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

More Apache Spark cons

"Azure Stream Analytics is challenging to customize because it's not very flexible."

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

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

"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."

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

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

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

More Azure Stream Analytics cons

Pricing and Cost Advice

"Licensing costs can vary. For instance, when purchasing a virtual machine, you're asked if you want to take advantage of the hybrid benefit or if you prefer the license costs to be included upfront by the cloud service provider, such as Azure. If you choose the hybrid benefit, it indicates you already possess a license for the operating system and wish to avoid additional charges for that specific VM in Azure. This approach allows for a reduction in licensing costs, charging only for the service and associated resources."

"Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."

"I did not pay anything when using the tool on cloud services, but I had to pay on the compute side. The tool is not expensive compared with the benefits it offers. I rate the price as an eight out of ten."

"We are using the free version of the solution."

"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

"Apache Spark is an expensive solution."

"Apache Spark is not too cheap. You have to pay for hardware and Cloudera licenses. Of course, there is a solution with open source without Cloudera."

"It is an open-source platform. We do not pay for its subscription."

More Apache Spark pricing and cost advice

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

"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."

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

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

"Azure Stream Analytics is a little bit expensive."

"The current price is substantial."

More Azure Stream Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

27%

Computer Software Company

13%

Manufacturing Company

Comms Service Provider

Computer Software Company

15%

Financial Services Firm

14%

Manufacturing Company

10%

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about Apache Spark?

We use Spark to process data from different data sources.

See all answers

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

Compared to other solutions like Doc DB, Spark is more costly due to the need for extensive infrastructure. It requires significant investment in infrastructure, which can be expensive. While cloud...

See all answers

What needs improvement with Apache Spark?

The Spark solution could improve in scheduling tasks and managing dependencies. Spark alone cannot handle sequential tasks, requiring environments like Airflow scheduler or scripts. For instance, o...

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

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AWS Batch vs Apache Spark

Compared 12% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 7% of the time

Amazon EMR vs Apache Spark

Compared 3% of the time

More Apache Spark Competitors

Databricks vs Azure Stream Analytics

Compared 26% of the time

Amazon MSK vs Azure Stream Analytics

Compared 24% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 19% of the time

Apache Flink vs Azure Stream Analytics

Compared 16% of the time

Apache Spark Streaming vs Azure Stream Analytics

Compared 3% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Spark

April 2025

Download Apache Spark product report

Buyer's Guide

Azure Stream Analytics

April 2025

Download Azure Stream Analytics product report

Also Known As

No data available

ASA

Overview

Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

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

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

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