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 8.7, and holds a 17.7% mindshare in the category. Microsoft is ranked #4 in SA , with an average rating of 8.1, and holds a 11.4% 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 65 Apache Spark reviews

1,728 Views
1,331 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 25 Azure Stream Analytics reviews

6,265 Views
5,355 Comparison Views

92% willing to recommend

Apache Spark

Azure Stream Analytics

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Executive SummaryUpdated on Jan 12, 2025

Azure Stream Analytics and Apache Spark are advanced data processing tools competing in the space of real-time analytics and large-scale data processing. Azure Stream Analytics leads in seamless integration with Microsoft services, while Apache Spark excels in comprehensive tools for big data processing.

Features: Azure Stream Analytics offers real-time analytics capabilities with SQL-like query support, easy Microsoft integrations, and reliable data streaming solutions. Apache Spark provides large-scale data processing, in-memory processing for fast computations, and robust machine learning capabilities through MLlib.

Room for Improvement: Apache Spark needs a simpler GUI, better real-time processing support, and enhanced scalability for more efficient setups. Azure Stream Analytics requires improvements in handling large datasets, better cross-cloud compatibility, and more diverse output options.

Ease of Deployment and Customer Service: Apache Spark can be deployed on various platforms with community-driven support, which may be challenging for newcomers. Azure Stream Analytics, being cloud-based, benefits from comprehensive Microsoft support, making it user-friendly and well-supported.

Pricing and ROI: Azure Stream Analytics, while expensive, offers a flexible pay-as-you-go model suitable for smaller projects. Apache Spark, as an open-source solution, incurs primary costs in infrastructure and support, delivering high ROI through efficient processing and cost savings at large scales.

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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 (4th)

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 17.7%, down 20.8% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 11.4% mindshare, down 12.9% 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

Sudhendra Umarji

Technical Architect at Infosys

Easy to set up and user-friendly, but could be priced better

I haven't come across missing items. It does what I need it to do. The pricing is a little bit high. The UI should be a little bit better from a usability perspective. The endpoint, if you are outsourcing to a third party, should have easier APIs. I'd like to have more destination sources available to us.

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 Spark can do large volume interactive data analysis."

"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 most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations."

"Apache Spark is known for its ease of use. Compared to other available data processing frameworks, it is user-friendly."

"Spark is used for transformations from large volumes of data, and it is usefully distributed."

"Apache Spark provides a very high-quality implementation of distributed data processing."

"The scalability has been the most valuable aspect of the solution."

"Spark can handle small to huge data and is suitable for any size of company."

More Apache Spark pros

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

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

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

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

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

"Technical support is pretty helpful."

"It provides the capability to streamline multiple output components."

"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."

More Azure Stream Analytics pros

Cons

"More ML based algorithms should be added to it, to make it algorithmic-rich for developers."

"The solution must improve its performance."

"When using Spark, users may need to write their own parallelization logic, which requires additional effort and expertise."

"The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate."

"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."

"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."

"Technical expertise from an engineer is required to deploy and run high-tech tools, like Informatica, on Apache Spark, making it an area where improvements are required to make the process easier for users."

"We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time."

More Apache Spark cons

"More flexibility in terms of writing queries and accommodating additional facilities would be beneficial."

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

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

"Early in the process, we had some issues with stability."

"The UI should be a little bit better from a usability perspective."

"We would like to have centralized platform altogether since we have different kind of options for data ingestion. Sometimes it gets difficult to manage different platforms."

"If something goes wrong, it's very hard to investigate what caused it and why."

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

More Azure Stream Analytics cons

Pricing and Cost Advice

"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."

"On the cloud model can be expensive as it requires substantial resources for implementation, covering on-premises hardware, memory, and licensing."

"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."

"The solution is affordable and there are no additional licensing costs."

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

"The product is expensive, considering the setup."

"They provide an open-source license for the on-premise version."

"Considering the product version used in my company, I feel that the tool is not costly since the product is available for free."

More Apache Spark pricing and cost advice

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

"The current price is substantial."

"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."

"Azure Stream Analytics is a little bit expensive."

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

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

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

"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."

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

Retailer

Computer Software Company

15%

Financial Services Firm

14%

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?

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 main concern is the overhead of Java when distributed processing is not necessary. In such cases, operations can often be done on one node, making Spark's distributed mode unnecessary. Conseque...

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?

Stream Analytics is cheaper, especially for small-scale requirements or telemetry needs. However, for enterprise-level data handling, structured streaming with Databricks might be more cost-effecti...

See all answers

What needs improvement with Azure Stream Analytics?

More flexibility in terms of writing queries and accommodating additional facilities would be beneficial. The complexity of handling messages that need decoding and contain different characters sho...

See all answers

Comparisons

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Compared 27% of the time

SAP HANA vs Apache Spark

Compared 12% of the time

AWS Batch vs Apache Spark

Compared 12% of the time

Cloudera Distribution for Hadoop vs Apache Spark

Compared 9% of the time

Apache NiFi vs Apache Spark

Compared 4% of the time

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Databricks vs Azure Stream Analytics

Compared 28% of the time

Amazon MSK vs Azure Stream Analytics

Compared 21% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 20% of the time

Apache Flink vs Azure Stream Analytics

Compared 14% of the time

Confluent vs Azure Stream Analytics

Compared 4% of the time

More Azure Stream Analytics Competitors

Product Reports

Buyer's Guide

Apache Spark

February 2025

Download Apache Spark product report

Buyer's Guide

Azure Stream Analytics

February 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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