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Apache Spark vs Azure Stream Analytics comparison

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Comparison Buyer's Guide
Executive Summary
Updated on September 5, 2022

We performed a comparison between Apache Spark vs.Azure Stream Analytics based on our users’ reviews in five categories. After reading all of the collected data, you can find our conclusion below.

  • Ease of Deployment: U users note that both products are very straightforward and simple to set up.
  • Features: Users of both products are generally happy with their flexibility, stability, and scalability. Some Azure Stream Analytics users noted issues with stability.

    Apache Spark users note being particularly satisfied with its AI libraries and batch processing, but that there’s a learning curve to using it and that its stream processing needs to be developed more.

    Azure Stream Analytics users say they’re impressed with the solution's UI, real-time analytics, and its deep integration with other Azure products. Some users mention issues when connecting to Microsoft Power BI and would like to see clearer metrics.
  • Pricing: Apache Spark is an open-source product. You have to pay only when you use any bundled product, such as Cloudera. Azure Stream Analytics users say that the solution is fairly priced and is cheaper than its biggest competitors.
  • ROI: Apache Spark users make no mention of ROI. Azure Stream Analytics users mention being pleased with the ROI.
  • Service and Support: Because Apache Spark is open-source, they do not offer support. Azure Stream Analytics users report excellent service and support.

Comparison Results: Apache Spark and Azure Stream Analytics come out about equal in this comparison. Some users are more satisfied with Apache Spark’s stability, and pricing, but Azure Stream Analytics has an edge when it comes to ROI and technical support.

To learn more, read our detailed Apache Spark vs. Azure Stream Analytics report (Updated: September 2022).
634,775 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
"Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term.""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.""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.""There's a lot of functionality.""Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica. Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark.""The solution has been very stable.""I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library.""This solution provides a clear and convenient syntax for our analytical tasks."

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"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.""The integrations for this solution are easy to use and there is flexibility in integrating the tool with Azure Stream Analytics.""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.""The solution has a lot of functionality that can be pushed out to companies.""I like the IoT part. We have mostly used Azure Stream Analytics services for it""It's a product that can scale.""The life cycle, report management and crash management features are great."

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"The initial setup was not easy.""Spark could be improved by adding support for other open-source storage layers than Delta Lake.""Apache Spark is very difficult to use. It would require a data engineer. It is not available for every engineer today because they need to understand the different concepts of Spark, which is very, very difficult and it is not easy to learn.""We are building our own queries on Spark, and it can be improved in terms of query handling.""Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors.""The logging for the observability platform could be better.""Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing.""This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."

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"Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure.""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.""The solution offers a free trial, however, it is too short.""The collection and analysis of historical data could be better.""The initial setup is complex.""The solution doesn't handle large data packets very efficiently, which could be improved upon.""The solution could be improved by providing better graphics and including support for UI and UX testing.""The UI should be a little bit better from a usability perspective."

More Azure Stream Analytics Cons →

Pricing and Cost Advice
  • "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."
  • "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."
  • More Apache Spark Pricing and Cost Advice →

  • "We pay approximately $500,000 a year. It's approximately $10,000 a year per license."
  • "I rate the price of Azure Stream Analytics a four out of five."
  • "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."
  • "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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    Questions from the Community
    Top Answer:I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer:It's an open-source product. I don't know much about the licensing aspect.
    Top Answer: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… more »
    Top Answer:I am unsure of what the licensing costs are for this solution.
    Top Answer:The product could be improved by providing more detailed analytics. For example, a graph to identify the past, present and current users. Additionally, UI and UX testing could be supported on this… more »
    out of 22 in Hadoop
    Average Words per Review
    out of 38 in Streaming Analytics
    Average Words per Review
    Also Known As
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    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

    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

    Learn more about Apache Spark
    Learn more about Azure Stream Analytics
    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab,, 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
    Top Industries
    Computer Software Company29%
    Financial Services Firm29%
    Marketing Services Firm7%
    Non Profit7%
    Financial Services Firm18%
    Computer Software Company18%
    Comms Service Provider15%
    Computer Software Company18%
    Comms Service Provider15%
    Financial Services Firm9%
    Energy/Utilities Company7%
    Company Size
    Small Business42%
    Midsize Enterprise23%
    Large Enterprise35%
    Small Business15%
    Midsize Enterprise12%
    Large Enterprise72%
    Small Business23%
    Midsize Enterprise8%
    Large Enterprise69%
    Small Business18%
    Midsize Enterprise12%
    Large Enterprise70%
    Buyer's Guide
    September 2022
    Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: September 2022.
    634,775 professionals have used our research since 2012.

    Apache Spark is ranked 1st in Hadoop with 13 reviews while Azure Stream Analytics is ranked 4th in Streaming Analytics with 11 reviews. Apache Spark is rated 8.2, while Azure Stream Analytics is rated 7.8. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of Azure Stream Analytics writes "A serverless scalable event processing engine with a valuable IoT feature". Apache Spark is most compared with Spring Boot, AWS Batch, AWS Lambda, SAP HANA and Apache NiFi, whereas Azure Stream Analytics is most compared with Amazon Kinesis, Databricks, Apache Flink, Apache Spark Streaming and Amazon MSK.

    We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.