Documentation

Introduction to Sparvi

What is Sparvi?

Sparvi is a comprehensive data observability platform that helps you catch data issues early and resolve what impacts business.

Sparvi Cloud

A full-featured data observability platform for data teams, featuring:

  • Interactive data exploration and lineage discovery
  • Anomaly detection dashboards with business impact analysis
  • Issue management with stakeholder notifications
  • Team collaboration and automated monitoring
  • Enterprise security with SSO and role-based access

Sparvi Cloud helps data teams maintain high-quality data, prevent issues before they impact business operations, and build confidence in their data assets.

Core Concepts

Data Observability

Data observability provides comprehensive visibility into your data's health, quality, and relationships across your organization. Sparvi implements data observability through:

  • Data Profiling: Automated analysis of data structure, completeness, and statistical patterns
  • Lineage Discovery: Understanding how data flows through your systems and who depends on it
  • Anomaly Detection: Identifying unusual patterns and data quality issues with business context
  • Issue Management: Tracking and resolving data problems with stakeholder awareness
  • Business Impact Analysis: Understanding which teams and processes are affected by data issues

Validation Rules

Validation rules enforce business rules and data quality standards. Sparvi Cloud provides:

  • SQL-Based Rules: Define validation criteria using familiar SQL syntax
  • Health Dashboards: Visual monitoring of validation performance over time
  • Automated Scheduling: Run validations on configurable schedules
  • Issue Integration: Failed validations automatically create tracked issues with business context

Metadata Management

Sparvi automatically collects and tracks metadata changes across your data infrastructure:

  • Schema Change Detection: Identify new, modified, or deleted columns
  • Relationship Discovery: Find dependencies between tables, views, and downstream systems
  • Historical Tracking: Maintain a complete audit trail of all metadata changes
  • Impact Analysis: Understand which systems and teams are affected by changes

Anomaly Detection

Sparvi's anomaly detection identifies data quality issues and provides business context:

  • Configurable Algorithms: Multiple detection methods tailored to your data patterns
  • Severity Classification: Automatic prioritization based on business impact
  • Trend Analysis: Track anomaly patterns over time with visual dashboards
  • Stakeholder Notifications: Alert the right people when issues affect their work

Architecture Overview

Sparvi Cloud Architecture

  • Web Application: React-based frontend with comprehensive dashboards and workflows
  • API Layer: RESTful APIs for all platform functionality
  • Database Connectors: Native integrations with Snowflake (OAuth), PostgreSQL
  • Automation Engine: Scheduled data profiling, validation runs, and metadata collection
  • Notification System: Multi-channel alerting with Azure Communication Services
  • Enterprise Security: Multi-factor authentication, role-based access, user management

Key Features

  • Data Explorer: Navigate database schemas, tables, and columns with comprehensive metadata
  • Lineage Discovery: Automatically map data relationships and business dependencies
  • Issue Management: Track and resolve data quality problems with business impact context
  • Anomaly Detection Dashboard: Visual monitoring with configurable detection algorithms
  • Team Collaboration: User management, notifications, and shared workflows
  • Enterprise Integrations: Snowflake OAuth, PostgreSQL, automated scheduling