About Dataiku

Dataiku is an all-in-one data science and machine learning solution designed for business analysts, data scientists, and software engineers. Users can create, deploy and reuse custom applications that utilize integrated data and machine learning to streamline data preparation/analysis, pipeline automation, statistical analysis, and model development.

Dataiku supports four machine learning engines: Spark, TensorFlow Python, and H20 as well as 32 different core algorithms. Dataiku drag-and-drop interface can be utilized at any step within the data management process from preparation to analysis to modeling. With more than 30 connectors on hand, users can easily integrate data from multiple systems into Dataiku centralized data system.


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5 Reviews of Dataiku

Average User Ratings

Overall

4.6 / 5 stars

Ease-of-use

5.0

Value for money

4.5

Customer support

4.0

Functionality

4.5

Ratings Snapshot

5 stars

(3)

3

4 stars

(2)

2

3 stars

(0)

0

2 stars

(0)

0

1 stars

(0)

0

Likelihood to Recommend

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October 2021

User Profile Picture

Vivek from Bridgei2i Analytics Solutions

Verified Reviewer

Company Size: 1,001-5,000 employees

Industry: Information Technology and Services

Time Used: Less than 2 years

Review Source: Capterra


Ease-of-use

5.0

Value for money

5.0

Customer support

5.0

Functionality

5.0

October 2021

Dataiku - Future of Data Science Platform

Whether the project requires data accumulation, or preprocessing, or data manipulation, or extracting business insights from data, Dataiku is the go-to platform. It covers end-to-end project execution and deployment along with providing API support.

Pros

time saving, useful for both coders and non-coders, allows for multi-user collaboration and monitoring, creating flowcharts help in maintaining granularity

Cons

It is in the early phase of its launch, 8 years old precisely. Hence, the customer support is not as widespread as other customer supports like stackoverflow, etc.

April 2021

Anonymous

Verified Reviewer

Company Size: 10,000+ employees

Time Used: Less than 6 months

Review Source: Capterra


Ease-of-use

5.0

Value for money

4.0

Customer support

4.0

Functionality

4.0

April 2021

Makes Advanced Analytics User Friendly

Overall even at the high cost I would recommend this to enable business users since the value proposition is very good. Perhaps no other tool in the market that makes data analytics so accessible

Pros

Intuitive UI for business users to interact with data in an excel like fashion and still be able to run advanced analytics on the data sets. Collaboration is effective.

Cons

It is expensive for IT to implement. They have packaged easily open source available code but have put in a user friendly UI. Data processing charges are high compared to competition.

April 2015

Vincent from Data-Business

Time Used: Free Trial

Review Source: Capterra


Ease-of-use

5.0

Customer support

4.0

April 2015

Making Kaggle Submissions with DSS

As a non - data scientist, i was curious to see how DSS could help me with the data preparation (cleaning and combining data), feature engineering and predictive modelling phases of a data analysis project My goal was to make 2 submissions on Kaggle challenges in under 1 hour and without 1 line of code using the Data Science Studio (Titanic and Otto Product Classification datasets). First, I was really impressed with the overall ease of use and ergonomy of the studio. Building "recipes" for data preparation mostly uses visual processors and the operations are visible directly on a sample of the data, facilitating validation of preparation steps. In a train / test scenario, i especially enjoyed being able to replicate my recipes on both datasets very easily. I used the Data Visualization tool to build a few exploratory charts, which can be done quite easily, though it is not as powerful as specialized tools (namely Tableau or Qlik). For the machine learning part, I restricted myself to visual machine learning in the studio, which already packs the most common algorithms (random forest, logistic, svm, gradient-boosting...). I found the ability to benchmark and compare algorithms performance quickly a great time saver, allowing me to reach a first score in under half an hour on each dataset. Once I chose the best model, I only needed a few clicks to use the model to prepare and score the Test Dataset and make my submissions. Both times I was in the lower half of the rankings but above Kaggle algorithmic benchmarks. For "real" Data Scientists and engineers, the Studio allows them to go much further by building recipes and models in R, Python, SQL, Hive, Pig etc...but even as a business analyst, I felt empowered by the software that enabled me to prepare, analyse and build simple predictive models with my data.

December 2018

Sana Kanwar from UNIVERSITY OF UTAH

Company Size: 1,001-5,000 employees

Industry: Education Management

Time Used: Less than 6 months

Review Source: Capterra


Ease-of-use

5.0

Customer support

3.0

Functionality

4.0

December 2018

Data science friendly

Pros

It helps me explore various anaylitical domains. Makes life easy and provides accurate results

Cons

Not much resources to learn from about the software

November 2015

Hugo from RESAE

Time Used: Free Trial

Review Source: Capterra


Ease-of-use

5.0

Customer support

5.0

November 2015

Excellent software for data and business teams collaboration in building data science applications

Easier way for data and business teams collaboration aiming to build data science applications