KDQ

Meet KDQ. Kada’s new data quality module.

In today’s data-driven world, ensuring high-quality, reliable data is critical for making informed business decisions.

KDQ provides a powerful, flexible, and business friendly approach to defining, managing, and tracking data quality to ensure that your data is accurate, consistent, and trustworthy.

[Add diagram for KDQ like we have for K]

Key Capabilities:

Rule-Based Data Validation
Define custom data quality rules based on your business logic, ensuring that your data meets predefined expectations. From simple checks (e.g., completeness, uniqueness) to complex validations (e.g., cross-column dependencies, statistical thresholds), KDQ offers a highly flexible approach.

Data Quality Monitoring
Continuously track and assess data quality over time with automated rule execution. Receive real-time alerts and insights when data falls outside expected parameters, preventing bad data from impacting critical processes.

Seamless Integration
KDQ’s Data Quality Module integrates effortlessly with modern data stacks, supporting a wide range of databases, data warehouses, and ETL pipelines. Whether you're using SQL, Spark, or cloud data platforms, KDQ fits right in.

Historical Tracking & Trend Analysis
Monitor data quality over time with built-in reporting and trend analysis. Identify patterns, detect data drift, and proactively address anomalies before they become issues.

Flexible & Scalable Architecture
Whether you're validating small datasets or managing large-scale enterprise data, KDQ scales with your needs. Define rules once and apply them across multiple datasets and pipelines without additional overhead.

Integrated Data Governance & Compliance
Ensure compliance with industry regulations and internal data governance policies. KDQ helps organizations enforce data integrity standards, maintain audit trails, and document data quality expectations.