Dataiku vs Dremio comparison

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Dataiku Logo
9,109 views|7,135 comparisons
100% 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 Dataiku 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 quite stable.""Data Science Studio's data science model is very useful.""Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors.""I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person.""The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction.""Cloud-based process run helps in not keeping the systems on while processes are running.""The most valuable feature is the set of visual data preparation tools."

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"Dremio allows querying the files I have on my block storage or object storage.""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.""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.""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."

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Cons
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin.""The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective.""Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable.""I find that it is a little slow during use. It takes more time than I would expect for operations to complete.""Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days).""I think it would help if Data Science Studio added some more features and improved the data model.""The ability to have charts right from the explorer would be an improvement.""There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."

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"It shows errors sometimes.""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.""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.""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.""We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."

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Pricing and Cost Advice
  • "The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."
  • More Dataiku 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."
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    Questions from the Community
    Top Answer:Databricks and Dataiku are excellent Data Science platforms but have different strengths and weaknesses. Below is a comparison of the two products based on several parameters Cost It is… more »
    Top Answer:Hi, I am the founder of Actable AI so my answer may be biased. In terms of performance, it's Actable AI. Why? Because we leverage the best and latest open source technologies out there (AutoGluon… more »
    Top Answer:Dataiku is my choice as it's not bulky and the learning path for people like me (noobs in ML and data science) is not steep at all, so after a couple of pieces of training I feel very confident. Also… more »
    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
    11th
    Views
    9,109
    Comparisons
    7,135
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    9th
    Views
    2,683
    Comparisons
    2,043
    Reviews
    6
    Average Words per Review
    530
    Rating
    8.7
    Comparisons
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    RapidMiner logo
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    IBM SPSS Statistics logo
    Compared 1% of the time.
    Databricks logo
    Compared 43% of the time.
    Snowflake logo
    Compared 17% of the time.
    Starburst Enterprise logo
    Compared 12% of the time.
    Amazon Redshift logo
    Compared 4% of the time.
    BigQuery logo
    Compared 2% of the time.
    Also Known As
    Dataiku DSS
    Learn More
    Dremio
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    Overview

    Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.

    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
    BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto
    UBS, TransUnion, Quantium, Daimler, OVH
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm18%
    Educational Organization14%
    Manufacturing Company8%
    Computer Software Company8%
    VISITORS READING REVIEWS
    Financial Services Firm31%
    Computer Software Company11%
    Manufacturing Company8%
    Retailer4%
    Company Size
    REVIEWERS
    Small Business57%
    Large Enterprise43%
    VISITORS READING REVIEWS
    Small Business12%
    Midsize Enterprise19%
    Large Enterprise68%
    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.

    Dataiku is ranked 11th in Data Science Platforms while Dremio is ranked 9th in Data Science Platforms with 6 reviews. Dataiku is rated 8.2, while Dremio is rated 8.6. The top reviewer of Dataiku writes "The model is very useful". On the other hand, the top reviewer of Dremio writes "It enables you to manage changes more effectively than any other platform". Dataiku is most compared with Databricks, KNIME, Alteryx, RapidMiner and IBM SPSS Statistics, whereas Dremio is most compared with Databricks, Snowflake, Starburst Enterprise, Amazon Redshift and BigQuery.

    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.