Dataiku

Visual Recipes and Pipelines

Drag-and-drop steps for joins, aggregates, and cleaning provide traceable pipelines you can audit. Switch to code for custom logic where needed, and use tests to validate outputs at each stage. Reusable components standardize best practices across projects. A shared canvas helps teams maintain clarity as datasets and flows grow, reducing regressions and making maintenance predictable over time.

AutoML and Notebooks

Build baseline models with AutoML, then refine in notebooks with features collaborators can inspect. Importance charts, confusion matrices, and ROC curves are visible to stakeholders. Environments isolate dependencies so results are reproducible, and registries track versions and approvals. Blending low-code and code-first keeps momentum for both analysts and engineers on the same platform.

Apps, Dashboards, and Sharing

Package pipelines behind interactive apps for business users and display metrics with filters and explanations. Permissions and reviewers protect sensitive data, while exports fit BI tools and presentations. Results travel beyond the data team in formats stakeholders can use immediately, closing the loop between exploration, decisions, and the reports leaders depend on to steer outcomes.

Automation and Scheduling

Scenarios trigger runs on schedules or events, with checks that watch for late data and anomalies. Notifications alert owners when steps fail, and audits record runs plus changes for accountability. Reliable automation keeps outputs fresh without manual babysitting, so downstream teams can trust that dashboards, apps, and feeds reflect current reality when they need to decide.

Governance and Security

Project-level policies set retention, masking, and access, while lineage shows how outputs were produced. RBAC and SSO control who can view or edit, and logs plus approvals support compliance efforts. Governed collaboration reduces risk while enabling broad participation in analytics, turning repeated analysis into maintained products that improve as requirements and data evolve.