Apache Spark Streaming vs Azure Stream Analytics comparison

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Executive Summary

We performed a comparison between Apache Spark Streaming and Azure Stream Analytics based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Apache Spark Streaming vs. Azure Stream Analytics Report (Updated: March 2024).
763,955 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:
Pros
"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple.""The solution is better than average and some of the valuable features include efficiency and stability.""Apache Spark Streaming has features like checkpointing and Streaming API that are useful.""The solution is very stable and reliable.""Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.""Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services.""As an open-source solution, using it is basically free.""It's the fastest solution on the market with low latency data on data transformations."

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"The way it organizes data into tables and dashboards is very helpful.""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.""The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.""The solution's most valuable feature is its ability to create a query using SQ.""I like the IoT part. We have mostly used Azure Stream Analytics services for it""The life cycle, report management and crash management features are great.""It provides the capability to streamline multiple output components."

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Cons
"In terms of improvement, the UI could be better.""The cost and load-related optimizations are areas where the tool lacks and needs improvement.""The initial setup is quite complex.""It was resource-intensive, even for small-scale applications.""We would like to have the ability to do arbitrary stateful functions in Python.""There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused.""The solution itself could be easier to use.""The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

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"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.""The UI should be a little bit better from a usability perspective.""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 could be improved by providing better graphics and including support for UI and UX testing.""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.""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.""It is not complex, but it requires some development skills. When the data is sent from Azure Stream Analytics to Power BI, I don't have the access to modify the data. I can't customize or edit the data or do some queries. All queries need to be done in the Azure Stream Analytics."

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Pricing and Cost Advice
  • "People pay for Apache Spark Streaming as a service."
  • "I was using the open-source community version, which was self-hosted."
  • "On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."
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  • "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."
  • "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."
  • "The current price is substantial."
  • "Azure Stream Analytics is a little bit expensive."
  • More Azure Stream Analytics Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.
    Top Answer:In terms of improvement, the UI could be better. Additionally, Spark Streaming works well for various use cases, but improvements could be made for ultra-fast scenarios where seconds matter. While… more »
    Top Answer:As a data engineer, I use Apache Spark Streaming to process real-time data for web page analytics and integrate diverse data sources into centralized data warehouses.
    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:Customers need to pay for a license. However, we have a three-year upfront licensing arrangement, which helps to keep the costs relatively low.
    Top Answer:Easier scalability and more detailed job monitoring features would be helpful. Another room for improvement is the ingestion of data.
    Ranking
    8th
    out of 38 in Streaming Analytics
    Views
    4,523
    Comparisons
    3,689
    Reviews
    6
    Average Words per Review
    473
    Rating
    8.2
    4th
    out of 38 in Streaming Analytics
    Views
    10,297
    Comparisons
    8,660
    Reviews
    12
    Average Words per Review
    379
    Rating
    8.3
    Comparisons
    Also Known As
    Spark Streaming
    ASA
    Learn More
    Overview

    Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

    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

    Sample Customers
    UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
    Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm19%
    Computer Software Company19%
    Comms Service Provider7%
    Manufacturing Company6%
    REVIEWERS
    Computer Software Company27%
    Manufacturing Company18%
    Insurance Company9%
    Government9%
    VISITORS READING REVIEWS
    Computer Software Company15%
    Financial Services Firm12%
    Manufacturing Company8%
    Comms Service Provider5%
    Company Size
    REVIEWERS
    Small Business56%
    Midsize Enterprise11%
    Large Enterprise33%
    VISITORS READING REVIEWS
    Small Business21%
    Midsize Enterprise12%
    Large Enterprise67%
    REVIEWERS
    Small Business25%
    Midsize Enterprise10%
    Large Enterprise65%
    VISITORS READING REVIEWS
    Small Business20%
    Midsize Enterprise11%
    Large Enterprise69%
    Buyer's Guide
    Apache Spark Streaming vs. Azure Stream Analytics
    March 2024
    Find out what your peers are saying about Apache Spark Streaming vs. Azure Stream Analytics and other solutions. Updated: March 2024.
    763,955 professionals have used our research since 2012.

    Apache Spark Streaming is ranked 8th in Streaming Analytics with 6 reviews while Azure Stream Analytics is ranked 4th in Streaming Analytics with 12 reviews. Apache Spark Streaming is rated 8.0, while Azure Stream Analytics is rated 8.2. The top reviewer of Apache Spark Streaming writes "Easy deployment as a cluster and good documentation". On the other hand, the top reviewer of Azure Stream Analytics writes "Offers advanced features and flavors for data processing and analysis". Apache Spark Streaming is most compared with Amazon Kinesis, Spring Cloud Data Flow, Confluent, Apache Pulsar and Starburst Enterprise, whereas Azure Stream Analytics is most compared with Amazon Kinesis, Databricks, Amazon MSK, Apache Flink and Confluent. See our Apache Spark Streaming vs. Azure Stream Analytics report.

    See our list of best Streaming Analytics vendors.

    We monitor all Streaming Analytics 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.