Dataiku vs Dremio comparison

Dataiku and Dremio are both solutions in the Data Science Platforms category. Dataiku is ranked #7 with an average rating of 7.5, while Dremio is ranked #8 with an average rating of 8.7. Dataiku holds a 11.5% mindshare in Data Science Platforms, compared to Dremio’s 4.1% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 100% of Dremio users who would recommend it.

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Executive Summary

We performed a comparison between Dataiku and Dremio based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Dataiku vs. Dremio Report (Updated: October 2024).

Buyer's Guide

Dataiku vs. Dremio

October 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% compared to the previous year. The mindshare of Dataiku is 11.5%, up from 7.5% compared to the previous year. The mindshare of Dremio is 4.1%, up from 2.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

AbakarAhmat

Software Engineer at a university with 10,001+ employees

Enhancing survey analysis that provides valued insightfulness

I used traditional tools where I would prepare data, click through menus, and use SQL Server for data visualization. We switched to IBM SPSS because it offers strong certification and aligns well with the standards we prioritize in our surveys. In terms of popularity, it stands out as the top choice in the market, especially in the research and university domains. Many different organizations and institutions use SPSS for statistical analytics. While there are other tools like MCLab and similar options available, SPSS is the most renowned and widely used among them.

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Sabrine Bendimerad

Data Scientist at Electricite De France

Saves a lot of time because I can quickly handle all the data preparation tasks and concentrate on building my machine learning algorithms

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated. While it was theoretically possible to use GitHub with Dataiku, in practice, it was difficult to manage our code effectively and push it from Dataiku to GitHub. Another limitation was its ability to handle different types of data. While Dataiku is powerful for working with structured data, like regular or geospatial data, it struggled with more complex data types such as text and image. In addition to the challenges with GitHub integration, the limited support for diverse data types was another feature lacking at that time.

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MikeWalker

Founder/ CEO at Morphogen Data Inc.

It enables you to manage changes more effectively than any other platform.

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. There's another thing called data providence. They're tied together. Data providence allows you to go back and recreate the data at any particular point in time. It's extremely important for compliance and governance issues because data changes all time. How did it change? What was it three days or months ago? You may have made some decisions based on data that was three months old, so you might need to revisit those. It's essential for things like machine learning and deep learning, where you are generating AI models off data. When the model stops working or doesn't work as expected, you need to figure out why. You have to go back and adjust the datasets used to train the model. We do that through an open-source project called Nessie, which is their basis for providing data lineage and data province capabilities. It's super powerful. Arrow is another open-source project for storing data in memory and performing data query operations. Data sits on a disk in one format. If you want to do anything with data, you have to load it into your computer and put it into memory so you can work with it. Arrow provides a format in memory that enables the whole library to perform various operations on that data. Every vendor has its own way of representing data in memory. They've latched onto an industry standard and developed it so it's open. Now people can use the exact same format in memory to do operations and use the library set to perform functions on data. New developers can decide if they want to develop their own memory format or use one that's already there. Data transfer is a massive problem when you're working with large datasets, doing advanced analytics, and trying to train machine learning or deep learning models. What happens often is companies downsample their data sets to do training on models because transferring and managing data on a deep learning or machine learning platform is too much.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Most of the product features are good but I particularly like the linear regression analysis."

"The most valuable feature is its robust statistical analysis capabilities."

"The features that I have found most valuable are the Bayesian statistics and descriptive statistics."

"You can quickly build models because it does the work for you."

"Some of the most valuable features that we are using with some business models are machine learning algorithms, statistical models given to us by the business, and getting data from the database or text files."

"The most valuable feature is the user interface because you don't need to write code."

"The learning curve to using this product is not steep. The program is appropriate for those who do not have a lot of background in programming, yet have to perform basic statistical analysis."

"SPSS is quite robust and quicker in terms of providing you the output."

More IBM SPSS Statistics pros

"The advantage is that you can focus on machine learning while having access to what they call 'recipes.' These recipes allow me to preprocess and prepare data without writing any code."

"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."

"The solution is quite stable."

"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

"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."

"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."

"Data Science Studio's data science model is very useful."

"The most valuable feature is the set of visual data preparation tools."

More Dataiku pros

"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."

"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."

"We primarily use Dremio to create a data framework and a data queue."

"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."

Cons

"The solution needs more planning tools and capabilities."

"There is a learning curve; it's not very steep, but there is one."

"Technical support needs some improvement, as they do not respond as quickly as we would like."

"The product should provide more ways to import data and export results that are user-friendly for high-level executives."

"I think the visualization and charting should be changed and made easier and more effective."

"I know that SPSS is a statistical tool but it should also include a little bit of analytical behavior. You can call it augmented analysis or predictive analysis. The bottom line is it should have more graphical and analytical capabilities."

"The reports could be better."

"The solution could improve by providing a visual network for predictions and a self-organizing map for clustering."

More IBM SPSS Statistics cons

"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."

"One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integrating GitHub with Dataiku was complicated."

"The ability to have charts right from the explorer would be an improvement."

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

"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."

"I think it would help if Data Science Studio added some more features and improved the data model."

More Dataiku cons

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

"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."

"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."

"It shows errors sometimes."

"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."

Pricing and Cost Advice

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"More affordable training for new staff members."

"The price of IBM SPSS Statistics could improve."

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"The price of this solution is a little bit high, which was a problem for my company."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"We think that IBM SPSS is expensive for this function."

More IBM SPSS Statistics 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."

"Pricing is pretty steep. Dataiku is also not that cheap."

"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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Top Industries

By visitors reading reviews

Financial Services Firm

17%

University

Computer Software Company

Manufacturing Company

Financial Services Firm

18%

Educational Organization

16%

Manufacturing Company

Computer Software Company

Financial Services Firm

32%

Computer Software Company

10%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

See all answers

What is your experience regarding pricing and costs for IBM SPSS Statistics?

The cost of IBM SPSS Statistics is managed by organizations, not individual researchers. It is a very expensive produ...

See all answers

What needs improvement with IBM SPSS Statistics?

IBM SPSS Statistics does not keep you close to your data like KNIME. In KNIME, at every stage, you can see the result...

See all answers

What is your experience regarding pricing and costs for Dataiku Data Science Studio?

It is very expensive.

See all answers

What needs improvement with Dataiku Data Science Studio?

One of the main challenges was collaboration. Developers typically use GitHub to push and manage code, but integratin...

See all answers

What is your primary use case for Dataiku Data Science Studio?

We use the solution for data science and machine learning.

See all answers

What do you like most about Dremio?

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

See all answers

What is your experience regarding pricing and costs for Dremio?

Every tool has a value based on its visualization, and the pricing is worth its value.

See all answers

What needs improvement with Dremio?

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 c...

See all answers

Comparisons

IBM SPSS Modeler vs IBM SPSS Statistics

Compared 14% of the time

Alteryx vs IBM SPSS Statistics

Compared 14% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 13% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

KNIME vs IBM SPSS Statistics

Compared 2% of the time

More IBM SPSS Statistics Competitors

Databricks vs Dataiku

Compared 38% of the time

KNIME vs Dataiku

Compared 15% of the time

Alteryx vs Dataiku

Compared 11% of the time

RapidMiner vs Dataiku

Compared 11% of the time

IBM SPSS Modeler vs Dataiku

Compared 1% of the time

More Dataiku Competitors

Databricks vs Dremio

Compared 48% of the time

Snowflake vs Dremio

Compared 14% of the time

Starburst Enterprise vs Dremio

Compared 8% of the time

Microsoft Power BI vs Dremio

Compared 4% of the time

Microsoft Azure Synapse Analytics vs Dremio

Compared 2% of the time

More Dremio Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

November 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Dataiku

November 2024

Download Dataiku product report

Buyer's Guide

Dremio

November 2024

Download Dremio product report

Also Known As

SPSS Statistics

Dataiku DSS

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IBM

Dataiku

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Dremio

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

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

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

UBS, TransUnion, Quantium, Daimler, OVH

Buyer's Guide

Dataiku vs. Dremio

October 2024

Free Report: Dataiku vs. Dremio

Find out what your peers are saying about Dataiku vs. Dremio and other solutions. Updated: October 2024.

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