Anaconda vs Databricks comparison

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2,965 views|2,207 comparisons
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30,172 views|19,226 comparisons
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Anaconda and Databricks based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms.
To learn more, read our detailed Data Science Platforms Report (Updated: March 2024).
765,386 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 virtual environment is very good.""The most advantageous feature is the logic building.""The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code.""I can use Anaconda for non-heavy tasks.""The documentation is excellent and the solution has a very large and active community that supports it.""The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly.""It helped us find find the optimal area for where our warehouse should be located.""The most valuable feature is the set of libraries that are used to support the functionality that we require."

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"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks.""The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly.""Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client.""Databricks helps crunch petabytes of data in a very short period of time.""Databricks has helped us have a good presence in data.""The solution is an impressive tool for data migration and integration.""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.""We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"

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Cons
"One feature that I would like to see is being able to use a different language in a different cell, which would allow me to mix R and Python together.""The interface could be improved. Other solutions, like Visual Studio, have much better UI.""Having a small guide or video on the tool would help learn how to use it and what the features are.""I think that the framework can be improved to make it easier for people to discover and use things on their own.""Anaconda can't handle heavy workloads.""It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database.""Anaconda should be optimized for RAM consumption.""I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."

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"Implementation of Databricks is still very code heavy.""Would be helpful to have additional licensing options.""Doesn't provide a lot of credits or trial options.""The product needs samples and templates to help invite users to see results and understand what the product can do.""Databricks can improve by making the documentation better.""There should be better integration with other platforms.""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.""Databricks could improve in some of its functionality."

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Pricing and Cost Advice
  • "The licensing costs for Anaconda are reasonable."
  • "The product is open-source and free to use."
  • "My company uses the free version of the tool. There is also a paid version of the tool available."
  • More Anaconda 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:Pricing is a matter of open source versus proprietary. Anaconda is open source and openly publishes their pricing models. RapidMiner is proprietary and you must receive a quote depending on your use… 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
    18th
    Views
    2,965
    Comparisons
    2,207
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    1st
    Views
    30,172
    Comparisons
    19,226
    Reviews
    47
    Average Words per Review
    446
    Rating
    8.2
    Comparisons
    Also Known As
    Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
    Learn More
    Overview

    Anaconda makes it easy for you to install and maintain Python environments. Our development team tests to ensure compatibility of Python packages in Anaconda. We support and provide open source assurance for packages in Anaconda to mitigate your risk in using open source and meet your regulatory compliance requirements.

    Python is the fastest growing language for data science. Anaconda includes 720+ Python open source packages and now includes essential R packages. This powerful combination allows you to do everything you want from BI to advanced modeling on complex Big Data

    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
    LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
    Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
    Top Industries
    REVIEWERS
    Financial Services Firm27%
    Manufacturing Company18%
    Non Tech Company9%
    Energy/Utilities Company9%
    VISITORS READING REVIEWS
    Financial Services Firm17%
    Computer Software Company10%
    Government10%
    Manufacturing Company6%
    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 Business39%
    Midsize Enterprise6%
    Large Enterprise56%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise13%
    Large Enterprise69%
    REVIEWERS
    Small Business27%
    Midsize Enterprise14%
    Large Enterprise59%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise11%
    Large Enterprise71%
    Buyer's Guide
    Data Science Platforms
    March 2024
    Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    Anaconda is ranked 18th in Data Science Platforms with 14 reviews while Databricks is ranked 1st in Data Science Platforms with 77 reviews. Anaconda is rated 7.8, while Databricks is rated 8.2. The top reviewer of Anaconda writes "Offers free version and is helpful to handle small-scale workloads". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Anaconda is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI, IBM SPSS Statistics and KNIME, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Microsoft Azure Machine Learning Studio, Dataiku Data Science Studio and Azure Stream Analytics.

    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.