One integrated platform. Everything your data team needs.

Container-native pipeline engine with three modes: X→Y (batch/on-demand), Change Data Capture (log, query, and trigger-based), and Advance ETL over 2000+ connectors.

Visual no-code canvas for Spark transformation pipelines, plus a Jupyter notebook surface. Batch, streaming, and on-demand, the same engine, auto-scaled on Kubernetes.

AutoML, visual pipelines, and Jupyter in one place. One-click model deployment as versioned REST APIs. Drift detection and experiment tracking built in.

25+ time-series algorithms: ARIMA, SARIMA, Prophet, LightGBM, XGBoost, N-BEATS, and more. Scheduled runs, accuracy dashboards, and side-by-side backtests.

Near-real-time anomaly detection on SQL, Kafka, webhooks, and APIs. Continuous re-learning with separate workspaces for monitoring and model configuration.

Autonomous root-cause analysis. No-code decision trees execute on Spark, isolate failure points, and verify remediation with a closed-loop health check.

No-code workflow automation. Event or schedule-driven; conditional logic, action blocks, and first-class integration with external systems.

Expose any SQL or NoSQL source as a versioned, secured REST API. Row and column-level security, rate limiting, auth, and auto-generated Swagger included.

Dashboards, chart widgets, and tabular reports with a visual query builder. Scheduled delivery via email, SFTP, or API. RBAC-governed access end to end.

Cross-tool lineage, automated discovery, business glossary, classification, and automated PII tagging. One catalog for every module.

Operational command centre: live pipeline health, SLA tracking, failure-pattern analysis, queue and resource trends, and AI-generated recommendations.

Unified RBAC, user and role management, data-source administration, and BI enablement. One security model for every module.