Decube Data Trust Platform for the AI Era

What is Decube?

Decube offers a comprehensive Data Trust Platform designed for the AI era, unifying data observability, discovery, and governance capabilities. It aims to help organizations build trust in their data by ensuring quality, reliability, and adherence to governance standards across their modern data stack. The platform provides a centralized solution for managing data assets effectively, facilitating data-driven decisions and preparing data for advanced analytics and AI applications.

Key functionalities include ML-powered anomaly detection to proactively identify and mitigate data quality issues, alongside robust data cataloging for thorough asset discovery and understanding. Decube features column-level lineage mapping for end-to-end data flow transparency and efficient root-cause analysis. It also supports data contracts to enforce quality standards between data producers and consumers, pipeline observability for monitoring ETL job performance, and automated data governance through advanced classification, tagging, and policy management. An integrated AI assistant, Decube CoPilot, enhances productivity with personalized support, intelligent metadata curation, Text2SQL conversion, and automated data quality suggestions. The platform emphasizes security and compliance, adhering to SOC 2, ISO 27001, HIPAA, and GDPR standards.

Features

Data Observability: Detects schema changes, duplicates, nulls, and anomalies using ML models.
Data Catalog: Enables discovery, understanding, and organization of data assets.
Data Governance: Simplifies governance with automated policy management, classification, and tagging.
Column-level Lineage: Traces data flow from source to target for root cause analysis.
Pipeline Observability: Monitors ETL job progress and performance with real-time visibility and alerts.
Data Contracts: Enforces data quality standards and facilitates collaboration between data producers and consumers.
Decube CoPilot (AI Assistant): Provides personalized assistance, intelligent metadata curation, Text2SQL conversion, and automated data quality suggestions.
Custom Data Validation: Allows defining custom tests using SQL or no-code configuration.
Integration Support: Connects with various data sources, BI tools, and communication platforms (e.g., Slack, MS Teams).

Use Cases

Ensuring data quality and reliability for accurate reporting and decision-making.
Improving data discovery and understanding across different teams.
Implementing and managing data governance policies and compliance.
Troubleshooting data pipeline issues efficiently through root cause analysis.
Preparing high-quality, trusted data for AI and Machine Learning models.
Facilitating collaboration and trust between data producers and consumers.
Monitoring and optimizing data pipeline performance and reliability.
Automating metadata management and data quality rule enforcement.