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 8.0, and holds a 11.1% 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

1,580 Views
1,196 Comparison Views

90% willing to recommend

Azure Stream Analytics

Read 25 Azure Stream Analytics reviews

5,402 Views
4,568 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 (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 21.2% compared to last year.
Azure Stream Analytics, on the other hand, focuses on Streaming Analytics, holds 11.1% mindshare, down 12.8% 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 processing time is very much improved over the data warehouse solution that we were using."

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

"We use it for ETL purposes as well as for implementing the full transformation pipelines."

"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."

"The distribution of tasks, like the seamless map-reduce functionality, is quite impressive."

"The data processing framework is good."

"The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics."

"It is useful for handling large amounts of data. It is very useful for scientific purposes."

More Apache Spark 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."

"It's scalable as a cloud product."

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

"Provides deep integration with other Azure resources."

"I like all the connected ecosystems of Microsoft, it is really good with other BI tools that are easy to connect."

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

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

"The way it organizes data into tables and dashboards is very helpful."

More Azure Stream Analytics pros

Cons

"It should support more programming languages."

"Apache Spark's GUI and scalability could be improved."

"It's not easy to install."

"Apache Spark lacks geospatial data."

"The logging for the observability platform could be better."

"For improvement, I think the tool could make things easier for people who aren't very technical. There's a significant learning curve, and I've seen organizations give up because of it. Making it quicker or easier for non-technical people would be beneficial."

"It would be beneficial to enhance Spark's capabilities by incorporating models that utilize features not traditionally present in its framework."

"There were some problems related to the product's compatibility with a few Python libraries."

More Apache Spark cons

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

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

"The initial setup is complex."

"The only challenge was that the streaming analytics area in Azure Stream Analytics could not meet our company's expectations, making it a component where improvements are required."

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

"There may be some issues when connecting with Microsoft Power BI because we are providing the input and output commands, and there's a chance of it being delayed while connecting."

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

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

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."

"The product is expensive, considering the setup."

"Spark is an open-source solution, so there are no licensing costs."

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

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

"It is an open-source solution, it is free of charge."

"Apache Spark is an open-source tool."

"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."

More Apache Spark pricing and cost advice

"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 current price is substantial."

"Azure Stream Analytics is a little bit expensive."

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

"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."

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

"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."

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

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?

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?

When I purchased the product, it was comparable to other Azure products. From my point of view, it should be cheaper now, considering the years since its release. The Azure solution is better now, ...

See all answers

What needs improvement with Azure Stream Analytics?

See all answers

Comparisons

Spring Boot vs Apache Spark

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

More Apache Spark Competitors

Databricks vs Azure Stream Analytics

Compared 28% of the time

Amazon MSK vs Azure Stream Analytics

Compared 22% of the time

Amazon Kinesis vs Azure Stream Analytics

Compared 18% of the time

Apache Flink vs Azure Stream Analytics

Compared 15% 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

March 2025

Download Apache Spark product report

Buyer's Guide

Azure Stream Analytics

March 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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