Azure Stream Analytics vs Databricks comparison

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10,297 views|8,660 comparisons
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9,698 views|6,228 comparisons
Comparison Buyer's Guide
Executive Summary
Updated on Jul 11, 2022

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

  • Ease of Deployment: Users of both solutions agree that their initial setup is straightforward.

  • Features: Users of both products are satisfied with their scalability and stability.

    Azure Stream Analytics reviewers praise its provisioning and IoT hub and say it provides them with good visibility into their system but that it can be difficult to troubleshoot.

    Databricks users say it has good artificial intelligence capabilities, is flexible with good integration options, and is robust and user friendly, but that it should accommodate more open-source products.
  • Pricing: Most Azure Stream Analytics reviewers feel that it is a fairly priced solution. In contrast, Most Databricks users feel that it is expensive.

  • Service and Support: Reviewers of both solutions report being very satisfied with the level of support they receive.

  • ROI: Users of both solutions report seeing an ROI.

Comparison Results: Databricks is the winner in this comparison. It is stable and powerful with good machine learning features. Azure Stream Analytics does come out on top in the pricing category, however.

To learn more, read our detailed Azure Stream Analytics vs. Databricks Report (Updated: March 2024).
765,234 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
"The most valuable features of Azure Stream Analytics are the ease of provisioning and the interface is not terribly complex.""We find the query editor feature of this solution extremely valuable for our business.""It's scalable as a cloud product.""The most valuable features are the IoT hub and the Blob storage.""The way it organizes data into tables and dashboards is very helpful.""Real-time analytics is the most valuable feature of this solution. I can send the collected data to Power BI in real time.""Provides deep integration with other Azure resources.""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 →

"It is fast, it's scalable, and it does the job it needs to do.""Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform.""Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user.""The most valuable feature of Databricks is the notebook, data factory, and ease of use.""The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient.""Imageflow is a visual tool that helps make it easier for business people to understand complex workflows.""The most valuable feature is the ability to use SQL directly with Databricks.""We have the ability to scale, collaborate and do machine learning."

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Cons
"The solution’s customer support could be improved.""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.""The solution offers a free trial, however, it is too short.""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.""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.""Its features for event imports and architecture could be enhanced.""Sometimes when we connect Power BI, there is a delay or it throws up some errors, so we're not sure."

More Azure Stream Analytics Cons →

"Doesn't provide a lot of credits or trial options.""Databricks doesn't offer the use of Python scripts by itself and is not connected to GitHub repositories or anything similar. This is something that is missing. if they could integrate with Git tools it would be an advantage.""It's not easy to use, and they need a better UI.""There is room for improvement in visualization.""It should have more compatible and more advanced visualization and machine learning libraries.""Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present.""It would be nice to have more guidance on integrations with ETLs and other data quality tools.""Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."

More Databricks Cons →

Pricing and Cost Advice
  • "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."
  • "The product's price is at par with the other solutions provided by the other cloud service providers in the market."
  • More Azure Stream Analytics Pricing and Cost Advice →

  • "Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."
  • "I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."
  • "Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
  • "We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."
  • "The pricing depends on the usage itself."
  • "I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."
  • "The price is okay. It's competitive."
  • "Databricks uses a price-per-use model, where you can use as much compute as you need."
  • More Databricks Pricing and Cost Advice →

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    Questions from the Community
    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.
    Top Answer:Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with… more »
    Top Answer:We researched AWS SageMaker, but in the end, we chose Databricks Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It… more »
    Ranking
    4th
    out of 38 in Streaming Analytics
    Views
    10,297
    Comparisons
    8,660
    Reviews
    12
    Average Words per Review
    379
    Rating
    8.3
    1st
    out of 38 in Streaming Analytics
    Views
    9,698
    Comparisons
    6,228
    Reviews
    47
    Average Words per Review
    446
    Rating
    8.2
    Comparisons
    Also Known As
    ASA
    Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
    Learn More
    Overview

    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

    Databricks is an industry-leading data analytics platform which is a one-stop product for all data requirements. Databricks is made by the creators of Apache Spark, Delta Lake, ML Flow, and Koalas. It builds on these technologies to deliver a true lakehouse data architecture, making it a robust platform that is reliable, scalable, and fast. Databricks speeds up innovations by synthesizing storage, engineering, business operations, security, and data science.

    Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This enables users to easily manage a colossal amount of data and to continuously train and deploy machine learning models for AI applications. The platform handles all analytic deployments, ranging from ETL to models training and deployment.

    Databricks deciphers the complexities of processing data to empower data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads. The program takes advantage of AI’s cost-effectivity, flexibility, and cloud storage.

    Databricks Key Features

    Some of Databricks key features include:

    • Cloud-native: Works well on any prominent cloud provider.
    • Data storage: Stores a broad range of data, including structured, unstructured, and streaming.
    • Self-governance: Built-in governance and security controls.
    • Flexibility: Flexible for small-scale jobs as well as running large-scale jobs like Big Data processing because it’s built from Spark and is specifically optimized for Cloud environments.
    • Data science tools: Production-ready data tooling, from engineering to BI, AI, and ML.
    • Familiar languages: While Databricks is Spark-based, it allows commonly used programming languages like R, SQL, Scala, and Python to be used.
    • Team sharing workspaces: Creates an environment that provides interactive workspaces for collaboration, which allow multiple members to collaborate for data model creation, machine learning, and data extraction.
    • Data source: Performs limitless Big Data analytics by connecting to Cloud providers AWS, Azure, and Google, as well as on-premises SQL servers, JSON and CSV.

    Reviews from Real Users

    Databricks stands out from its competitors for several reasons. Two striking features are its collaborative ability and its ability to streamline multiple programming languages.

    PeerSpot users take note of the advantages of these features. A Chief Research Officer in consumer goods writes, “We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.”

    A business intelligence coordinator in construction notes, “The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes.”

    An Associate Manager who works in consultancy mentions, “The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view. The ability to migrate from one environment to another is useful.”

    Sample Customers
    Rockwell Automation, Milliman, Honeywell Building Solutions, Arcoflex Automation Solutions, Real Madrid C.F., Aerocrine, Ziosk, Tacoma Public Schools, P97 Networks
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    Top Industries
    REVIEWERS
    Computer Software Company27%
    Manufacturing Company18%
    Insurance Company9%
    Government9%
    VISITORS READING REVIEWS
    Computer Software Company15%
    Financial Services Firm12%
    Manufacturing Company8%
    Comms Service Provider5%
    REVIEWERS
    Computer Software Company25%
    Financial Services Firm16%
    Retailer9%
    Manufacturing Company9%
    VISITORS READING REVIEWS
    Financial Services Firm14%
    Computer Software Company12%
    Manufacturing Company8%
    Healthcare Company6%
    Company Size
    REVIEWERS
    Small Business24%
    Midsize Enterprise10%
    Large Enterprise67%
    VISITORS READING REVIEWS
    Small Business20%
    Midsize Enterprise11%
    Large Enterprise69%
    REVIEWERS
    Small Business27%
    Midsize Enterprise14%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    Buyer's Guide
    Azure Stream Analytics vs. Databricks
    March 2024
    Find out what your peers are saying about Azure Stream Analytics vs. Databricks and other solutions. Updated: March 2024.
    765,234 professionals have used our research since 2012.

    Azure Stream Analytics is ranked 4th in Streaming Analytics with 21 reviews while Databricks is ranked 1st in Streaming Analytics with 77 reviews. Azure Stream Analytics is rated 8.0, while Databricks is rated 8.2. The top reviewer of Azure Stream Analytics writes "Easy to set up and user-friendly, but could be priced better". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Azure Stream Analytics is most compared with Amazon Kinesis, Amazon MSK, Apache Flink, Apache Spark and Apache Spark Streaming, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Dremio. See our Azure Stream Analytics vs. Databricks 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.