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Apache Spark Streaming vs Azure Stream Analytics comparison

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Featured Review
Find out what your peers are saying about Apache Spark Streaming vs. Azure Stream Analytics and other solutions. Updated: January 2022.
566,121 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The solution is very stable and reliable.""The solution is better than average and some of the valuable features include efficiency and stability."

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"The solution has a lot of functionality that can be pushed out to companies.""Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time.""Technical support is pretty helpful.""Provides deep integration with other Azure resources.""The most valuable features are the IoT hub and the Blob storage.""I like the IoT part. We have mostly used Azure Stream Analytics services for it""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."

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Cons
"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."

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"The collection and analysis of historical data could be better.""The solution offers a free trial, however, it is too short.""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.""Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure.""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.""If something goes wrong, it's very hard to investigate what caused it and why.""Early in the process, we had some issues with stability."

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Pricing and Cost Advice
Information Not Available
  • "The cost of this solution is less than competitors such as Amazon or Google Cloud."
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    Questions from the Community
    Top Answer: 
    The solution is better than average and some of the valuable features include efficiency and stability.
    Top Answer: 
    There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused. For example, it is still not plug and play and use as… more »
    Top Answer: 
    The primary use of the solution is to implement predictive maintenance qualities.
    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: 
    In terms of pricing, you can't compare it to open source solutions. It would be higher compared to open source, of course, however, with the support and everything you're getting, I would say the… more »
    Top Answer: 
    With Azure specifically, the drawback is it is a very Azure-specific product. You can't connect it to external things out of Azure. For example, Spark or Databricks can be used in any cloud and can be… more »
    Ranking
    9th
    out of 38 in Streaming Analytics
    Views
    3,506
    Comparisons
    3,248
    Reviews
    2
    Average Words per Review
    334
    Rating
    7.5
    5th
    out of 38 in Streaming Analytics
    Views
    8,022
    Comparisons
    6,978
    Reviews
    7
    Average Words per Review
    784
    Rating
    8.0
    Comparisons
    Also Known As
    Spark Streaming
    ASA
    Learn More
    Overview

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

    AzureStream Analytics is a fully managed event-processing engine that lets you set up real-time analytic computations on streaming data.The data can come from devices, sensors, web sites, social media feeds, applications, infrastructure systems, and more.
    Offer
    Learn more about Apache Spark Streaming
    Learn more about Azure Stream Analytics
    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
    Computer Software Company28%
    Comms Service Provider17%
    Financial Services Firm8%
    Media Company8%
    VISITORS READING REVIEWS
    Computer Software Company28%
    Comms Service Provider21%
    Energy/Utilities Company6%
    Financial Services Firm6%
    Company Size
    No Data Available
    REVIEWERS
    Small Business29%
    Midsize Enterprise14%
    Large Enterprise57%
    Find out what your peers are saying about Apache Spark Streaming vs. Azure Stream Analytics and other solutions. Updated: January 2022.
    566,121 professionals have used our research since 2012.

    Apache Spark Streaming is ranked 9th in Streaming Analytics with 2 reviews while Azure Stream Analytics is ranked 5th in Streaming Analytics with 7 reviews. Apache Spark Streaming is rated 7.6, while Azure Stream Analytics is rated 8.0. The top reviewer of Apache Spark Streaming writes "Mature and stable with good scalability". 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 Streaming is most compared with Amazon Kinesis, Spring Cloud Data Flow, Databricks, Confluent and Talend Data Streams, whereas Azure Stream Analytics is most compared with Databricks, Apache Spark, Apache Flink, Apache NiFi and Amazon Kinesis. 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.