Databricks vs Dataiku comparison

Databricks and Dataiku are both solutions in the Data Science Platforms category. Databricks is ranked #1 with an average rating of 8.3, while Dataiku is ranked #6 with an average rating of 8.2. Databricks holds a 18.2% mindshare in Data Science Platforms, compared to Dataiku’s 12.7% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Dataiku users who would recommend it.

Databricks

Read 88 Databricks reviews

14,742 Views
10,545 Comparison Views

96% willing to recommend

Dataiku

Read 12 Dataiku reviews

6,785 Views
5,638 Comparison Views

100% willing to recommend

Databricks

Dataiku

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 12, 2025

Databricks and Dataiku are prominent products competing in the realm of data analytics and artificial intelligence. Databricks might have an edge due to its high-speed analytics and integration, while Dataiku is favored for user-friendly interfaces and extensive data integration capabilities.

Features: Databricks offers efficient large-scale analytics, automated cluster creation, and collaborative notebooks. It supports popular programming languages like SQL and Python, making it a versatile choice for developers. Dataiku presents visual data preparation tools, flexibility in using various coding languages, and seamless data handling directly into BigQuery, which is beneficial for non-technical users.

Room for Improvement: Databricks could benefit from enhanced visualization tools, improved integration features, and a more transparent pricing structure. The user experience could be more intuitive for non-programmers. Dataiku faces collaboration challenges, complex data type handling issues, and partial reliance on coding, which can be barriers for users seeking low-code or no-code solutions.

Ease of Deployment and Customer Service: Databricks provides a versatile deployment environment across public, private, and hybrid clouds, with generally positive customer support, though some users experience delays. Dataiku is mostly on-premises, with limited cloud deployment options, and its thorough documentation reduces the need for frequent technical support, showcasing its ease of use and setup efficiency.

Pricing and ROI: Databricks is perceived as expensive but competitive, offering strong ROI due to high performance and scalability despite a complex licensing model. Dataiku also tends to be costly upfront but is praised for its cost-effectiveness in complex analytics and machine learning tasks. Databricks' pay-as-you-go model may lead to cost savings on extensive workloads, while Dataiku offers potential savings through efficient data handling and preparation.

To learn more, read our detailed Databricks vs. Dataiku Report (Updated: March 2025).

Buyer's Guide

Databricks vs. Dataiku

March 2025

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Review summaries and opinions

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

Categories and Ranking

Databricks

Ranking in Data Science Platforms

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (7th), Streaming Analytics (1st)

Dataiku

Ranking in Data Science Platforms

6th

Average Rating

8.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Databricks is 18.2%, down from 19.1% compared to the previous year. The mindshare of Dataiku is 12.7%, up from 8.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

ShubhamSharma7

Data Engineer at a engineering company with 1,001-5,000 employees

Capability to integrate diverse coding languages in a single notebook greatly enhances workflow

Databricks offers various courses that I can use, whether it's PySpark, Scala, or R. I can leverage all these courses in a single notebook, which is beneficial for clients as they can access various tools in one place whenever needed. This is quite significant. I usually work with PySpark based on client requirements. After coding, I feed the Databricks notebooks into the ADF pipeline for updates. Databricks' capability to process data in parallel enhances data processing speed. Furthermore, I can connect our Databricks notebook directly with Power BI and other visualization tools like Qlik. Once we develop code, it allows us to transform raw data into visualizations for clients using analysis diagrams, which is very helpful.

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RichardXu

Data Science Lead at a mining and metals company with 10,001+ employees

The platform organizes workflows visually and efficiently

One of the valuable features of Dataiku is the workflow capability. It allows us to organize a workflow efficiently. The platform has a visual interface, making it much easier for educated professionals to organize their work. This feature is useful because it simplifies tasks and eliminates the need for a data scientist. If you are knowledgeable about AI, you can directly write using primitive tools like Pantera flow, PyTorch, and Scikit-learn. However, Dataiku makes this process much easier.

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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

"It can send out large data amounts."

"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."

"The processing capacity is tremendous in the database."

"Databricks has a scalable Spark cluster creation process. The creators of Databricks are also the creators of Spark, and they are the industry leaders in terms of performance."

"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."

"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."

"There are good features for turning off clusters."

"It is a cost-effective solution."

More Databricks pros

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

"One of the valuable features of Dataiku is the workflow capability."

"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."

"Cloud-based process run helps in not keeping the systems on while processes are running."

"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 rate the overall product as eight out of ten."

"I believe the return on investment looks positive."

"The solution is quite stable."

More Dataiku pros

Cons

"This solution only supports queries in SQL and Python, which is a bit limiting."

"Databricks could improve in some of its functionality."

"The connectivity with various BI tools could be improved, specifically the performance and real time integration."

"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."

"Implementation of Databricks is still very code heavy."

"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."

"I believe that this product could be improved by becoming more user-friendly."

"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."

More Databricks cons

"One area for improvement is the need for more capabilities similar to those provided by NVIDIA for parallel machine learning training. We still encounter some integration issues."

"We still encounter some integration issues."

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

"The technical support from Dataiku is not good. The support team does not provide adequate assistance, and there are concerns about billing requests."

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

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

"There is room for improvement in terms of allowing for more code-based features."

"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."

More Dataiku cons

Pricing and Cost Advice

"The solution requires a subscription."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."

"The solution is a good value for batch processing and huge workloads."

"The price is okay. It's competitive."

"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."

"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."

"We only pay for the Azure compute behind the solution."

More Databricks 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."

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Top Industries

By visitors reading reviews

Financial Services Firm

17%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Financial Services Firm

17%

Educational Organization

14%

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

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 ...

See all answers

How would you compare Databricks vs Amazon SageMaker?

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...

See all answers

Which would you choose - Databricks or Azure Stream Analytics?

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 orga...

See all answers

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

The pricing for Dataiku is very high, which is its biggest downside. The model they follow is not consumption-based, making it expensive.

See all answers

What needs improvement with Dataiku Data Science Studio?

Dataiku's pricing is very high, and commercial transparency is a challenge. Support is also an area needing improvement. More features like LLM security, holographic encryption, and enhanced GPU in...

See all answers

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

My primary use case for Dataiku ( /products/dataiku-reviews ) is for data science, Gen ( /products/gen-reviews ) AI, and data science applications. Our AGN team also uses it for various purposes.

See all answers

Comparisons

Microsoft Power BI vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 8% of the time

Informatica PowerCenter vs Databricks

Compared 7% of the time

Tableau vs Databricks

Compared 5% of the time

Amazon SageMaker vs Databricks

Compared 5% of the time

More Databricks Competitors

KNIME vs Dataiku

Compared 18% of the time

Alteryx vs Dataiku

Compared 12% of the time

Altair RapidMiner vs Dataiku

Compared 10% of the time

Amazon SageMaker vs Dataiku

Compared 8% of the time

Microsoft Azure Machine Learning Studio vs Dataiku

Compared 5% of the time

More Dataiku Competitors

Product Reports

Buyer's Guide

Databricks

March 2025

Download Databricks product report

Buyer's Guide

Dataiku

March 2025

Download Dataiku product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Dataiku DSS

Overview

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Databricks

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.

Dataiku

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

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

Buyer's Guide

Databricks vs. Dataiku

March 2025

Free Report: Databricks vs. Dataiku

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