Dataiku vs Google Cloud Datalab comparison

Dataiku and Google are both solutions in the Data Science Platforms category. Dataiku is ranked #6 with an average rating of 8.2, while Google is ranked #16 with an average rating of 7.8. Dataiku holds a 12.8% mindshare in Data Science Platforms, compared to Google’s 1.0% mindshare. Additionally, 100% of Dataiku users are willing to recommend the solution, compared to 80% of Google users who would recommend it.

Dataiku

Read 12 Dataiku reviews

6,785 Views
5,638 Comparison Views

100% willing to recommend

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

711 Views
612 Comparison Views

80% willing to recommend

Dataiku

Google Cloud Datalab

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Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and Dataiku are products in the data analytics domain. Dataiku holds the advantage with its comprehensive features and user-friendly analytics tools.

Features: Google Cloud Datalab offers seamless integration with Google Cloud services, rapid notebook-based exploration, and real-time collaboration. Dataiku provides robust data preparation tools, machine learning capabilities, and easy-to-use analytics supporting teamwork.

Room for Improvement: Google Cloud Datalab could enhance its advanced analytics tools, improve user support for complex setups, and expand its non-Google Cloud integrations. Dataiku could benefit from reducing its initial setup complexity, offering more detailed customization options, and optimizing system resource usage for large datasets.

Ease of Deployment and Customer Service: Google Cloud Datalab ensures straightforward deployment in Google's ecosystem, leveraging Google's support. Dataiku offers flexible deployment on-premises or in the cloud with personalized support, facilitating a smoother transition.

Pricing and ROI: Google Cloud Datalab's integration lowers setup costs, appealing to budget-conscious users. Dataiku, despite higher initial costs, offers substantial ROI with its impactful features, justifying its pricing through efficiency in complex data tasks.

To learn more, read our detailed Dataiku vs. Google Cloud Datalab Report (Updated: April 2025).

Buyer's Guide

Dataiku vs. Google Cloud Datalab

April 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

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

Google Cloud Datalab

Ranking in Data Science Platforms

16th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (18th)

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Dataiku is 12.8%, up from 8.4% compared to the previous year. The mindshare of Google Cloud Datalab is 1.0%, down from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

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.

Read full review

Nilesh Gode

Project Manager - Data Science at Mettler-Toledo International Inc.

Easy to setup, stable and easy to design data pipelines

The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.

Read full review

Quotes from Members

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

Pros

"Our clients can easily drag and drop components and use them on the spot."

"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 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."

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

"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."

"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."

More Dataiku pros

"Google Cloud Datalab is very customizable."

"For me, it has been a stable product."

"The APIs are valuable."

"All of the features of this product are quite good."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

Cons

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

"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."

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

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

"The license is very expensive. It would be great to have an intermediate license for basic treatments that do not require extensive experience."

"The license is very expensive."

"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."

"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

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"The product must be made more user-friendly."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

"The interface should be more user-friendly."

Pricing and Cost Advice

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

"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."

"The product is cheap."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"It is affordable for us because we have a limited number of users."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

11%

Manufacturing Company

Computer Software Company

Financial Services Firm

20%

University

12%

Computer Software Company

10%

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

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

I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies. However, it is a pricey solution and I primarily recommend it to bigger companies.

See all answers

What needs improvement with Dataiku Data Science Studio?

There is room for improvement in terms of allowing for more code-based features. I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend ...

See all answers

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

My company sells licenses for both Dataiku and Alteryx, and we have clients who use them. I engage with several companies in telecommunications, retail, and energy to assess how our clients are uti...

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What do you like most about Google Cloud Datalab?

Google Cloud Datalab is very customizable.

See all answers

What needs improvement with Google Cloud Datalab?

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcin...

See all answers

What is your primary use case for Google Cloud Datalab?

It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...

See all answers

Comparisons

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KNIME vs Dataiku

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Alteryx vs Dataiku

Compared 12% of the time

Altair RapidMiner vs Dataiku

Compared 10% of the time

Amazon SageMaker vs Dataiku

Compared 9% of the time

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IBM SPSS Statistics vs Google Cloud Datalab

Compared 45% of the time

Databricks vs Google Cloud Datalab

Compared 31% of the time

Cloudera Data Science Workbench vs Google Cloud Datalab

Compared 24% of the time

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Product Reports

Buyer's Guide

Dataiku

April 2025

Download Dataiku product report

Buyer's Guide

Google Cloud Datalab

April 2025

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Also Known As

Dataiku DSS

No data available

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.

Dataiku

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

Google

Sample Customers

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

Information Not Available

Buyer's Guide

Dataiku vs. Google Cloud Datalab

April 2025

Free Report: Dataiku vs. Google Cloud Datalab

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