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Qupid Documentation

website·Indexed 30 days ago

https://learn.qupid.io/

Documentation for Qupid, a data quality and observability platform. Includes guides on data source integration for RDBMS, NoSQL, analytics platforms, and DWH solutions, as well as REST API references, Helm chart deployment, and RBAC models. Covers version updates from v2.4.0 through v3.2.0.

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What's inside Qupid

  1. Overview of Qupid Tutorial Tracks

    Qupid provides structured learning paths to help users operate its data quality and observability platform. The tracks are categorized by user expertise and goals:

    • Beginner Tutorials: Focuses on the essentials, including connecting the first data source, creating observers (static, SQL, or ML), and monitoring results.
    • Core Workflows: Covers advanced observer building, managing 'Spaces' for domain-level monitoring, and analyzing health scores and trends.
    • Advanced Deep Dives:
      • Explorer: Advanced filtering, Table Health/Profile, Lineage visualization, Anomaly Detection, and direct SQL queries.
      • Spaces: Organizing datasets and dashboards into logical workspaces for team collaboration.
      • Incidents: Managing incident triggers, root cause investigation, and resolution workflows.
      • BI Discovery: Integrating BI tools (Superset, Power BI, Tableau, Looker) to monitor dashboard coverage and link BI insights to data incidents.
    • Lineage & Integration: Engineering-focused guides for visualizing table dependencies and integrating with Airflow, dbt, and Spark, including translating table contracts into observer YAML.
    • Advanced Topics: Custom SQL-based rules, ML-based observer configuration, and alert automation.

  2. Overview of Qupid Quality Observability components

    Qupid's Quality Observability suite consists of the following core components:

    • Observers: The primary mechanism for monitoring, including their lifecycle and logs.
    • Observer Types: Available as either Static or ML-based observers.
    • Observer Categories: Pre-defined test groups covering Integrity, Uniqueness, Freshness, Volume, Drift, Schema, and Custom rules.
    • Rule Catalog: A comprehensive list of all built-in rules and templates available for implementation.

  3. Introduction to Qupid Data Quality and Observability Platform

    Qupid is a data quality and observability platform designed to help modern data teams solve data quality problems. The documentation is structured to support sequential learning for new users, as well as reference-based lookup for specific topics.

  4. Connect Qupid to external data sources

    Qupid supports integration with a wide range of data sources, including Relational Database Management Systems (RDBMS), NoSQL databases, Analytics platforms, Data Warehouse (DWH) solutions, Kafka, and Snowflake.

  5. Connect Qupid to external data sources

    Qupid supports integration with a wide variety of data sources, including Relational Database Management Systems (RDBMS), NoSQL databases, Analytics platforms, Data Warehouse (DWH) solutions, Kafka, and Snowflake.

  6. Connect to external data sources in Qupid

    Qupid supports integration with a wide range of data sources, including Relational Database Management Systems (RDBMS), NoSQL databases, Analytics platforms, Data Warehouse (DWH) solutions, Kafka, and Snowflake.

  7. Qupid REST API capabilities

    Qupid provides a RESTful API that allows developers to manage assets, rules, and incidents. The API includes support for authentication, pagination, filtering, webhooks, and rate limiting.

  9. Get started with Quality Observability

    To begin monitoring data quality in Qupid, follow this workflow:

    1. Review the Observers overview to understand the core concept, lifecycle, and logs.
    2. Select an Observer Category (e.g., Integrity, Uniqueness, Freshness, Volume, Drift, Schema, or Custom) and perform a test.
    3. Choose an Observer Type (Static or ML) and define the necessary thresholds.
    4. Schedule and monitor the quality runs.

  10. Utilize AI-powered data platform features

    The Qupid platform includes AI-driven capabilities such as anomaly detection, smart recommendations, the Qupid Assistant, and intelligent root cause analysis algorithms.