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Databricks vs KNIME comparison

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Featured Review
Buyer's Guide
Databricks vs. KNIME
July 2022
Find out what your peers are saying about Databricks vs. KNIME and other solutions. Updated: July 2022.
621,327 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
"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature.""This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities.""Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions.""Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good.""Databricks helps crunch petabytes of data in a very short period of time.""Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data.""The technical support is good.""I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."

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"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.""KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data.""From a user-friendliness perspective, it's a great tool.""We have found KNIME valuable when it comes to its visualization.""I was able to apply basic algorithms through just dragging and dropping.""The product is open-source and therefore free to use.""The solution is good for teaching, since there is no need to code.""KNIME is quite scalable, which is one of the most important features that we found."

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Cons
"Databricks can improve by making the documentation better.""Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks.""The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration.""It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them.""Anyone who doesn't know SQL may find the product difficult to work with.""Databricks' technical support takes a while to respond and could be improved.""There would also be benefits if more options were available for workers, or the clusters of the two points.""Databricks could improve in some of its functionality."

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"Both RapidMiner and KNIME should be made easier to use in the field of deep learning.""Not just for KNIME, but generally for software and analyzing data, I would welcome facilities for analyzing different sorts of scale data like Likert scales, Thurstone scales, magnitude ratio scales, and Guttman scales, which I don't use myself.""The documentation is lacking and it could be better.""I would prefer to have more connectivity.""There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger.""KNIME is not scalable.""From the point of view of the interface, they can do a little bit better.""There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."

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Pricing and Cost Advice
  • "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."
  • "There are different versions."
  • "The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."
  • "The solution requires a subscription."
  • "Databricks is a very expensive solution. Pricing is an area that could definitely be improved. They could provide a lower end compute and probably reduce the price."
  • More Databricks Pricing and Cost Advice →

  • "It's an open-source solution."
  • "The price for Knime is okay."
  • "At this time, I am using the free version of Knime."
  • "This is an open-source solution that is free to use."
  • "There is a Community Edition and paid versions available."
  • "KNIME assets are stand alone, as the solution is open source."
  • "With KNIME, you can use the desktop version free of charge as much as you like. I've yet to hit its limits. If I did, I'd have to go to the server version, and for that you have to pay. Fortunately, I don't have to at the moment."
  • "The client versions are mostly free, and we pay only for the KNIME server version. It's not a cheap solution."
  • More KNIME Pricing and Cost Advice →

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    Questions from the Community
    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 »
    Top Answer:We have found KNIME valuable when it comes to its visualization.
    Top Answer:The setup for KNIME is simple. I would rate the setup a five on a scale of one to five.
    Top Answer:KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile.
    Ranking
    1st
    Views
    38,032
    Comparisons
    30,386
    Reviews
    30
    Average Words per Review
    440
    Rating
    8.1
    4th
    Views
    15,787
    Comparisons
    12,004
    Reviews
    16
    Average Words per Review
    405
    Rating
    8.2
    Comparisons
    Also Known As
    Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
    KNIME Analytics Platform
    Learn More
    Overview

    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.”

    KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available. KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.

    KNIME Features

    KNIME has many valuable key features. Some of the most useful ones include:

    • Scalability through data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
    • High extensibility via a well-defined API for plugin extensions
    • Intuitive user interface
    • Import/export of workflows
    • Parallel execution on multi-core systems
    • Command line version for "headless" batch executions
    • Activity dashboard
    • Reporting & statistics
    • Third-party integrations
    • Workflow management
    • Local automation
    • Metanode linking
    • Tool blending
    • Big Data extensions

    KNIME Benefits

    There are many benefits to implementing KNIME. Some of the biggest advantages the solution offers include:

    • Integrated Deployment: KNIME’s integrated deployment moves both the selected model, and the entire data model preparation process into production simply and automatically, allowing for continuous optimization in production and also saving time because it eliminates error.
    • Elastic and Hybrid Execution: KNIME’s elastic and hybrid executions helps you reduce costs while covering periods of high demand, dynamically.
    • Metadata Mapping: KNIME enables complete metadata mapping of all aspects of your workflow. In addition, KNIME offers blueprint workflows for documenting the nodes, data sources, and libraries used, as well as runtime information.
    • Guided Analytics: KNIME’s guided analytics applications can be customized based on reusable components.
    • Powerful analytics, local automation, and workflow difference: KNIME uses advanced predictive and machine learning algorithms to provide you with the analytics you need. In combination with powerful analytics, KNIME’s automation capabilities and workflow difference prepare your organization with the tools you need to make better business decisions.
    • Supports enterprise-wide data science practices: The deployment and management functionalities of KNIME make it easy to productionize data science applications and services, and deliver usable, reliable, and reproducible insights for the business.
    • Helps you leverage insights gained from your data: Using KNIME ensures the data science process immediately reflects changing requirements or new insights.

    Reviews from Real Users

    Below are some reviews and helpful feedback written by PeerSpot users currently using the KNIME solution.

    An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”

    Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function. Technical support has been extremely responsive so far. The solution has a very strong and supportive community that shares information and helps each other troubleshoot. The solution is very stable. The initial setup is pretty simple and straightforward.”

    Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”

    Offer
    Learn more about Databricks
    Learn more about KNIME
    Sample Customers
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
    Top Industries
    REVIEWERS
    Financial Services Firm15%
    Computer Software Company15%
    Retailer10%
    Mining And Metals Company10%
    VISITORS READING REVIEWS
    Computer Software Company22%
    Comms Service Provider14%
    Financial Services Firm9%
    Manufacturing Company6%
    REVIEWERS
    University25%
    Retailer20%
    Comms Service Provider15%
    Government10%
    VISITORS READING REVIEWS
    Comms Service Provider21%
    Computer Software Company16%
    Manufacturing Company9%
    Financial Services Firm8%
    Company Size
    REVIEWERS
    Small Business25%
    Midsize Enterprise16%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise13%
    Large Enterprise72%
    REVIEWERS
    Small Business30%
    Midsize Enterprise27%
    Large Enterprise43%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise15%
    Large Enterprise69%
    Buyer's Guide
    Databricks vs. KNIME
    July 2022
    Find out what your peers are saying about Databricks vs. KNIME and other solutions. Updated: July 2022.
    621,327 professionals have used our research since 2012.

    Databricks is ranked 1st in Data Science Platforms with 30 reviews while KNIME is ranked 4th in Data Science Platforms with 15 reviews. Databricks is rated 8.0, while KNIME is rated 8.2. The top reviewer of Databricks writes "Good integration with majority of data sources through Databricks Notebooks using Python, Scala, SQL, R". On the other hand, the top reviewer of KNIME writes "Allows you to easily tidy up your data, make lots of changes internally, and has good machine learning". Databricks is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Dataiku Data Science Studio, Azure Stream Analytics and Microsoft BI, whereas KNIME is most compared with Alteryx, RapidMiner, Microsoft BI, Weka and IBM SPSS Modeler. See our Databricks vs. KNIME 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.