DataOps With Dataiku

Efficiently manage the entire data lifecycle by automating workflows, ensuring data quality, fostering collaboration, and providing seamless governance and monitoring, enabling teams to turn raw data into actionable insights with speed, consistency, and control.

Visualize & Manage Data Pipelines With the Flow

The Dataiku Flow provides a visual representation of data pipelines, making it easy for users to design, understand, and manage complex workflows. Each step in the Flow represents a data operation, from ingestion to transformation and preparation.

Easily alter the Flow with smart controls (e.g., insert recipe, delete and reconnect). With clear control of the pipeline, teams can quickly handle issues and optimize processes for greater efficiency.

Learn About the Dataiku Flow

Build Trusted Insights With Data Quality Checks

Ensure that data used in projects meets high quality standards with data quality rules. Dataiku provides visibility to detect and address data issues early, preventing poor data from compromising analytics and machine learning outcomes.

Rulesets can be templatized and applied across multiple datasets and projects, ensuring consistency throughout the data pipeline.

Dive Into Data Quality in Dataiku

Track Your Data's Journey With End-to-End Lineage

Visualize how data transforms across your organization with data lineage in Dataiku. Get an instant look into column relationships and dependencies. Quickly diagnose quality issues and assess downstream impacts of changes. Teams can confidently modify data pipelines knowing exactly how changes will affect their projects and deployments.

Explore Data Lineage in Dataiku

Streamline Workflows With Automation Scenarios

Scenarios in Dataiku automate routine data operations — from data preparation and transformation to model retraining and batch scoring. By creating customized triggers and workflows, teams can automate repetitive tasks, allowing data professionals to focus on strategic initiatives rather than manual processes.

Read About Scenarios and Automation in Dataiku

Our team members do not have to care about how they get data from A to B. They do not even have to think about how the project works in detail… The task works completely automatically. This allows us to spend time on other work. The motto of the project is ‘You need to transfer data? Go and do it easily.

Daniel Bindig

Data Engineer/Scientist at Galeria

Provide a Central Palace for Data Discovery & Governance

The Data Catalog in Dataiku centralizes metadata, making it easy to discover, document, and share datasets across teams. It includes governance features such as data lineage tracking and version control, ensuring transparency and consistency in data use.

With a well-organized data catalog, teams can avoid duplication and quickly find the datasets they need for their analytics activities. Now, all projects are built on reliable, up-to-date data.

See a Demo of the Dataiku Data Catalog

Deploy to Production With 1 Click

Dataiku makes it easy to deploy data pipelines to production using batch deployment and project bundles. Users can bundle entire projects — including datasets, flows, scenarios, and models — for seamless deployment in production environments.

Batch deployment enables pipelines to run on a scheduled basis, ensuring continuous data refreshes and timely delivery of insights.

Learn More About Data Governance

Go Further With DataOps

Learn More About Data Lineage in Dataiku

Learn How Data Quality Works in Dataiku

Try Out Data Quality Yourself With This Tutorial

Learn About Metrics In Dataiku

Explore the Dataiku Data Catalog

Contact Us

Interested in learning more about DataOps with Dataiku? Let's talk.