Anaconda vs Dremio comparison

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Anaconda Logo
2,704 views|2,040 comparisons
94% willing to recommend
Dremio Logo
2,683 views|2,043 comparisons
100% willing to recommend
Comparison Buyer's Guide
Executive Summary

We performed a comparison between Anaconda and Dremio 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: May 2024).
771,157 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 solution is stable.""The notebook feature is an improvement over RStudio.""It has a lot of functionality available, supports many libraries, and the developers are continually improving it.""Voice Configuration and Environmental Management Capabilities are the most valuable features.""The most valuable feature is the set of libraries that are used to support the functionality that we require.""The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly.""The product is responsive, sleek and has a beautiful interface that is pleasant to use. It helps users to easily share code.""It's interesting. It's user friendly. That's what makes it outstanding among the others. It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science."

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"We primarily use Dremio to create a data framework and a data queue.""The most valuable feature of Dremio is it can sit on top of any other data storage, such as Amazon S3, Azure Data Factory, SGFS, or Hive. The memory competition is good. If you are running any kind of materialized view, you'd be running in memory.""Dremio allows querying the files I have on my block storage or object storage.""Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it.""Everyone uses Dremio in my company; some use it only for the analytics function.""Dremio gives you the ability to create services which do not require additional resources and sterilization."

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Cons
"Anaconda can't handle heavy workloads.""When you install Anaconda for the first time, it's really difficult to update it.""Having a small guide or video on the tool would help learn how to use it and what the features are.""Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring.""I think that the framework can be improved to make it easier for people to discover and use things on their own.""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.""The solution would benefit from offering more automation.""A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."

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"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily.""Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement.""Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake.""I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported.""They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people.""It shows errors sometimes."

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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."
  • "The tool is open-source."
  • "Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks."
  • More Anaconda Pricing and Cost Advice →

  • "Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
  • "Dremio is less costly competitively to Snowflake or any other tool."
  • More Dremio Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.
    Top Answer:Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring.
    Top Answer:Dremio allows querying the files I have on my block storage or object storage.
    Top Answer:Every tool has a value based on its visualization, and the pricing is worth its value.
    Top Answer:Dremio's interface is good, but it has a few limitations. I cannot do a lot of things with ANSI SQL or basic SQL. I cannot use the recursive common table expression (CTE) in Dremio because the support… more »
    Ranking
    13th
    Views
    2,704
    Comparisons
    2,040
    Reviews
    2
    Average Words per Review
    457
    Rating
    8.0
    9th
    Views
    2,683
    Comparisons
    2,043
    Reviews
    6
    Average Words per Review
    530
    Rating
    8.7
    Comparisons
    Learn More
    Dremio
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    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

    Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.

    At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.

    What Are Dremio's Key Features?

    • Data Reflections: Reduces query times by creating optimized representations of source data, which can accelerate performance without the complexity of traditional data warehousing solutions.
    • Semantic Layer: Allows users to define business metrics and dimensions centrally, ensuring consistency and governance across all analytics tools.
    • Built-in Security Features: Provides robust security measures, including column- and row-level security, ensuring compliance with data governance and privacy standards.
    • Support for Multiple Data Formats and Sources: Enables querying directly against a variety of data formats (Parquet, JSON, etc.) and sources without the need for conversion or replication.

    What Benefits Should Users Expect?

    When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.

    How is Dremio Implemented Across Different Industries?

    Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.

    What About Dremio’s Pricing, Licensing, and Support?

    Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.

    Sample Customers
    LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
    UBS, TransUnion, Quantium, Daimler, OVH
    Top Industries
    REVIEWERS
    Financial Services Firm27%
    Manufacturing Company18%
    Non Tech Company9%
    Energy/Utilities Company9%
    VISITORS READING REVIEWS
    Financial Services Firm17%
    Computer Software Company9%
    Government9%
    Manufacturing Company6%
    VISITORS READING REVIEWS
    Financial Services Firm31%
    Computer Software Company11%
    Manufacturing Company8%
    Retailer4%
    Company Size
    REVIEWERS
    Small Business45%
    Midsize Enterprise5%
    Large Enterprise50%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise12%
    Large Enterprise70%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise11%
    Large Enterprise73%
    Buyer's Guide
    Data Science Platforms
    May 2024
    Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms. Updated: May 2024.
    771,157 professionals have used our research since 2012.

    Anaconda is ranked 13th in Data Science Platforms with 17 reviews while Dremio is ranked 9th in Data Science Platforms with 6 reviews. Anaconda is rated 8.0, while Dremio is rated 8.6. 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 Dremio writes "It enables you to manage changes more effectively than any other platform". Anaconda is most compared with Databricks, Microsoft Azure Machine Learning Studio, Amazon SageMaker, Microsoft Power BI and IBM SPSS Statistics, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and Microsoft Azure Synapse Analytics.

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    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.