Use Cases

Data Quality Triage

Add rich contextual data to help data engineers triage data quality alerts. Automatically run basic checks on source data sets to automate initial steps in triage runbook

Scan and tag PII/PHI and other sensitive data

Tag sensitive data in data manually or using scanners. Use column-level lineage to propogate tags to all data. Always have a completel catalog of sensitive data columns.

Programmatically monitor and manage access to sensitive data

Catalog users you have access to analyze sensitive data. Deep dive into how sensitive data is being used.

Data and ETL Pipeline Clean up

Analyze lineage and usage data to consolidate similar data sets, remove unused data sets and pipelines. Reduce cost as well as instances of sensitive data

Data Impact Analysis

Analyze impact of data infrastructure changes like adding a column or changing transformations.

How it Works

Ingest metadata from data warehouses

  • Query History
  • User information
  • Access Rights

Parse and organize information

  • Column-Level Data Lineage
  • Edges are associated with jobs that loaded the data
  • Columns are associated with user and access rights

Visual analysis and automation

  • Visualize using a user-friendly graph browser
  • Reports and analytics on data usage
  • Super charge data management with lineage graph algorithms