Dataiku Reviews, Ratings & Features 2026 | Gartner Peer Insights

Data preparation is an iterative and agile process for finding, combining, cleaning, transforming and sharing curated datasets for various data and analytics use cases including analytics/business intelligence (BI), data science/machine learning (ML) and self-service data integration.

Data preparation tools promise faster time to delivery of integrated and curated data by allowing business users including analysts, citizen integrators, data engineers and citizen data scientists to integrate internal and external datasets for their use cases. Furthermore, they allow users to identify anomalies and patterns and improve and review the data quality of their findings in a repeatable fashion. Some tools embed ML algorithms that augment and, in some cases, completely automate certain repeatable and mundane data preparation tasks. Reduced time to delivery of data and insight is at the heart of this market.

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Find the top Data Preparation Tools with Gartner. Compare and filter by verified product reviews and choose the software that’s right for your organization.

Data Preparation Tools

Dataiku

Engineering Manager

I have used Dataiku as an ETL tool and have utilized most of the features that have been provided, and the user interface is unmatched. The ease with which Dataiku handles large volumes of data is very good. 

1B - 3B USD

30B + USD

10B - 30B USD

50M - 250M USD

500M - 1B USD

Gov't/PS/ED <5,000 Employees

Gov't/PS/ED 5,000 - 50,000 Employees

<50M USD

250M - 500M USD

3B - 10B USD

Import Manager

Dataiki is very potent for data preparation and definitely gives high priority to the team where people interact. Nevertheless, the limitation of the scalability of the workflows which contain complex processes makes its dependent use in big data problems. This dataset is good when it comes to doing smaller to medium sized transformations; but when things become more intricate, users tend to get into a complicated labyrinth.

DATA ENGINEER

Pretty satisfied overall, happy with the customizability on the installation of the program as we're a govt entity that requires to have on prem solutions

Alteryx

Microsoft

Salesforce (Informatica)

Qlik

Salesforce (Tableau)

Other...

Google

DataRobot

Strategy

Qlik (Talend)

Trifacta

Zoho

Infoworks

Minitab

Dataiku is a single enterprise platform that unites people, orchestration, and governance to turn AI investments into measurable business outcomes. It helps organizations move from fragmented experimentation to coordinated, trusted execution at scale.
Built for AI Success: Dataiku brings business experts and AI specialists into the same environment, embedding business context into analytics, models, and AI agents. Business teams can self-serve and innovate, while AI experts build, deploy, and optimize quickly, closing the gap between pilots and production.
Orchestration That Scales: Dataiku connects data, AI services, and enterprise apps across analytics, machine learning, and AI agents. Integrated workflows deliver value across any cloud or infrastructure without vendor lock-in or fragmentation.
Governance You Can Trust: Dataiku embeds governance across the AI lifecycle, enabling teams to track performance, cost, and risk to keep systems explainable, compliant, and auditable.



Dataiku is the platform for Everyday AI, systemizing the use of data for exceptional business results. Organizations that use Dataiku elevate their people (whether technical and working in code or on the business side and low- or no-code) to extraordinary, arming them with the ability to make better day-to-day decisions with data.

More than 500 companies worldwide use Dataiku to systemize their use of data, analytics, and AI, driving diverse use cases from fraud detection to customer churn prevention, predictive maintenance to supply chain optimization, and everything in between. 

Reviews

Ratings Overview

Overall experience with

Favorable Review

Customizable Installation Praised; Workflow Debugging Remains a Challenge for Some

Critical Review

Feels awkward and lacks data preparation

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