Cancel
You must select at least 2 products to compare!
Alteryx Logo
12,334 views|7,198 comparisons
88% willing to recommend
Databricks Logo
28,492 views|18,008 comparisons
96% willing to recommend
Comparison Buyer's Guide
Executive Summary
Updated on Aug 30, 2022

We performed a comparison between Alteryx 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.

    Databricks users like the quick visualization it provides and say it is easy to use, flexible, and robust, but they would like it to support more open-source products.

    Alteryx reviewers like its drag-and-drop capabilities and user-friendliness, and say it has good modeling and scheduling features. A couple of users note that it can sometimes consume a lot of resources.

  • Pricing: Alteryx users say it is an expensive product. Databricks received mixed reviews in the pricing category. Some users feel that its price is too high.

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

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

Comparison Results: Databricks has a slight edge in this comparison. It received better marks in the pricing category than Alteryx did.

To learn more, read our detailed Alteryx vs. Databricks Report (Updated: May 2024).
771,740 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
"Alteryx is a low-code platform, and that's the biggest reason why we chose it.""The solution has a very strong community that is involved in the product. It helps make the usage easier and helps us find answers to our questions.""The most valuable feature of Alteryx is its unlimited handling capabilities.""The scheduling feature for the automation is excellent.""The philosophy of the citizen data scientist is the key piece, which means the no-code analytics capability. This is the feature that attracted us the most.""The product's Macros probably are one of the most useful aspects.""The data transformation feature is the most valuable. The ability to ingest data, visualize data, and transform that data is useful.""Alteryx has made us more agile and increased the speed and effectiveness of decision making."

More Alteryx Pros →

"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly.""It's easy to increase performance as required.""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.""The setup is quite easy.""Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance.""What I like about Databricks is that it's one of the most popular platforms that give access to folks who are trying not just to do exploratory work on the data but also go ahead and build advanced modeling and machine learning on top of that.""The initial setup is pretty easy.""Databricks' most valuable feature is the data transformation through PySpark."

More Databricks Pros →

Cons
"If there is any way to make the learning curve less steep, that would be ideal.""Configuration is very low.""What they're struggling with is it's not as mature as Tableau in the user management area. It was tougher to manage the server part of it right away, especially since the user base has grown.""Alteryx can improve in data science. They have to have more features and components in the data science aspect because they claim to be a data science tool. However, in order to be more competitive, they have to improve on their data science propositions. Thre are other solutions on the market, such as other players in the market, Data2Go or DataIQ, and Alteryx needs to catch up.""Alteryx could be improved in the area of analytics and central governance.""The only area where the product lags is documentation and videos on the analytical app and the batch macro.""The solution can be made more affordable.""It would be nice if they can provide Alteryx with more options for In-DB connectivity. That functionality is there, but it doesn't include all software we are connecting."

More Alteryx Cons →

"Anyone who doesn't know SQL may find the product difficult to work with.""In the future, I would like to see Data Lake support. That is something that I'm looking forward to.""The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice.""I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast.""The initial setup is difficult.""I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement.""The Databricks cluster can be improved.""The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."

More Databricks Cons →

Pricing and Cost Advice
  • "A designer and scheduler for $13K/year in total is pretty much earning you the money back in time and in other resources."
  • "​Very transparent.​"
  • "The seat is too expensive."
  • "It can be a bit pricey, especially after the first year."
  • "The pricing is $5000 per year per production license."
  • "We have a yearly cost that we pay for the licensing. We do not pay any costs in addition to the licensing fees."
  • "There are some implementation services and internal effort costs at the beginning but there is nothing else."
  • "The price for Alteryx Designer is reasonable but the price for Alteryx Server for universal collaborations is too expensive."
  • More Alteryx 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 →

    report
    Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.
    771,740 professionals have used our research since 2012.
    Questions from the Community
    Top Answer:One of the differences is that with Alteryx you can use it as an ETL and analytics tool. Please connect with me directly if you want to know more.
    Top Answer:Alteryx is an extremely easy and flexible data tool, flexible in terms of drag and drop toolset and also has python, R integrations if your team requires this It can handle over 2 billion rows of… more »
    Top Answer:I am not familiar with IBM SPSS Modeler, therefore, I cannot compare these two products Regarding Alteryx I can say the following: - An excellent desktop tool for Data Prep and analytics. -… more »
    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 »
    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 »
    Ranking
    3rd
    Views
    12,334
    Comparisons
    7,198
    Reviews
    29
    Average Words per Review
    513
    Rating
    8.2
    1st
    Views
    28,492
    Comparisons
    18,008
    Reviews
    47
    Average Words per Review
    441
    Rating
    8.3
    Comparisons
    KNIME logo
    Compared 14% of the time.
    Dataiku logo
    Compared 8% of the time.
    RapidMiner logo
    Compared 7% of the time.
    Microsoft Power BI logo
    Compared 6% of the time.
    Tableau logo
    Compared 5% of the time.
    Also Known As
    Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
    Learn More
    Overview

    Alteryx can be used to speed up or automate your business processes and enables geospatial and predictive solutions. Its platform helps organizations answer business questions quickly and efficiently, and can be used as a major building block in a digital transformation or automation initiative. With Alteryx, you can build processes in a more efficient, repeatable, and less error-prone way. Unlike other tools, Alteryx is easy to use without an IT background. The platform is very robust and can be used in virtually any industry or functional area.

    With Alteryx You Can:

    • Prep, blend, and analyze data
    • Deliver faster, better business outcomes
    • Automate analytics and data science
    • Embed intelligent decisioning
    • Deploy and share analytics in hours

    Alteryx Features Include:

    Some of the most valuable Alteryx features include:

    Scalability, stability, flexibility, fast performance, no-code analytics, data processing, business logic wrapping, scheduling, ease of use, data blending from different platforms, geo-referencing, good customization capabilities, drag and drop functionality, intuitive user interface, connectors, machine learning, macros, simple GUI, integration with Python, good data transformation, good documentation, multiple database merging, and easy deployment.

    Alteryx Can Be Used For:

    • Combining and manipulating data within spreadsheets: Alteryx can be used in situations where complex data manipulation occurs. It can handle large data quickly, and the process is much simpler to see and understand.

    • Database access and supplementing SQL development: Alteryx has several sets of database connectors and functions, including many functions that your average database does not. Alteryx can work with data from multiple databases or areas within a database. It allows users to filter, sort, calculate, etc. as they would commonly do in SQL or an ETL tool.
    • API, cloud, and hybrid access: Alteryx can read and write data in databases, files, REST APIs, and a myriad of other locations (with the correct permissions). When a workflow is published, you can also call a workflow through a REST API to start it.

    • Data science: Alteryx provides pre-built models that are extremely useful for data scientists who may have limited programming skills and also gives you the ability to add R or Python code directly within a workflow.

    • Geospatial analysis: Alteryx gives users drag-and-drop tools to geocode, plot, and map locations, customers, competitors, or anything that has a location (employee, truck, pipeline, etc.).

    • Reports and dashboards: Alteryx provides built-in tools that enable the building of reports and dashboards.

    Alteryx Benefits

    Some of the benefits of using Alteryx include
    :

    • Saves time: Alteryx helps shorten the time required to start analyzing data and looking for insights, minimizing the tasks that do not add value to the business and maximizing the analysis phase.

    • Clear tool configurations: Alteryx provides simple and concise tool configurations that are quick and easy to set.

    • Excellent workflow compatibility.

    • Reduced development time: Alteryx has an extensive gallery of user-developed analytic applications that helps to reduce development time.

    • Fast data loading: Alteryx has tools that make it very effective when working with big data sets.

    • No-code, low-code analytic building blocks: You can prep, blend, and analyze data to enable highly configurable and repeatable workflows.

    • Machine learning: Alteryx allows you to quickly create properly trained algorithms that are ready to deploy.

    Reviews from Real Users

    "Automation is the most valuable aspect for us. The ability to wrap business logic around the data is very helpful." - Theresa M., Senior Capacity Planner at a financial services firm

    "Alteryx has made us more agile and increased the speed and effectiveness of decision making." - Richard F., Director, Digital Experience & Media at Qdoba Restaurant Corporation

    "The scheduling feature for the automation is excellent." - Data Analytics Engineer at a tech services company

    "The product is very stable and super fast, five-star. It's significantly more stable than its nearest competitor." - Director at a non-tech company

    “A complete solution with very good user experience and a nice user interface.” - Solutions Consultant at a tech services company

    "There are a lot of good customization capabilities." - Advance Analytics PO at a pharma/biotech company





    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
    AnalyticsIq Inc., belk, BloominBrands Inc., Cardinalhealth, Cineplex, Dairy Queen
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    Top Industries
    REVIEWERS
    Computer Software Company15%
    Manufacturing Company11%
    Financial Services Firm11%
    Healthcare Company9%
    VISITORS READING REVIEWS
    Financial Services Firm22%
    Manufacturing Company10%
    Computer Software Company9%
    Retailer6%
    REVIEWERS
    Computer Software Company25%
    Financial Services Firm16%
    Manufacturing Company9%
    Retailer9%
    VISITORS READING REVIEWS
    Financial Services Firm15%
    Computer Software Company12%
    Manufacturing Company9%
    Healthcare Company6%
    Company Size
    REVIEWERS
    Small Business33%
    Midsize Enterprise15%
    Large Enterprise52%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise10%
    Large Enterprise74%
    REVIEWERS
    Small Business27%
    Midsize Enterprise14%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    Buyer's Guide
    Alteryx vs. Databricks
    May 2024
    Find out what your peers are saying about Alteryx vs. Databricks and other solutions. Updated: May 2024.
    771,740 professionals have used our research since 2012.

    Alteryx is ranked 3rd in Data Science Platforms with 74 reviews while Databricks is ranked 1st in Data Science Platforms with 78 reviews. Alteryx is rated 8.4, while Databricks is rated 8.2. The top reviewer of Alteryx writes "Feature-rich ETL that condenses a number of functions into one tool". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Alteryx is most compared with KNIME, Dataiku, RapidMiner, Microsoft Power BI and Tableau, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio and Apache Flink. See our Alteryx vs. Databricks report.

    See our list of best Data Science Platforms vendors.

    We monitor all Data Science Platforms 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.