Ensure the reliability and timeliness of data across your data landscape with Acceldata’s continuous and automated data quality monitoring platform.

Leverage anomaly detection and a flexible and highly scalable framework with policies for all the 6 Dimensions of Data Quality (accuracy, completeness, consistency, freshness, validity and uniqueness), data reconciliation, schema drift, and data drift.

Gain end-to-end visibility and insights into your data assets and pipelines from start to finish to ensure data gets delivered properly and on-time.

Eliminate operational blindspots and clogged, slow, inefficient or stopped pipelines by continuously observing pipelines such as Kafka and Airflow.

Detect problems at the beginning of your data landscape to isolate issues before they hit your Databricks Lakehouse or before they affect downstream analytics and consumption.

Implement the “1 x 10 x 100 Rule” of Data Quality that states that the cost of fixing a problem increases exponentially as you move from the source or ingestion zone to the consumption zone or Databricks Lakehouse, where it finally lands up

Get visibility into all your data in Databricks and across your entire data stack: data-at-rest, data-in-motion, and data-for-consumption.

Trace transformation failures and data inaccuracy across tables and columns with detailed data lineage and by pinpointing the exact root cause.