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Containers Overview

Containers are the fundamental entities in Qualytics that represent structured datasets. They include tables and views in JDBC datastores, files in DFS datastores, and computed derivatives built from them. Each container is the unit of profiling, quality checks, and anomaly detection.

After a Sync operation, each container is assigned a status (Available, Changed, Inaccessible, or Unloadable) based on its current state in the datastore.

Container Types

  • Container Types

    JDBC tables and views, DFS files, and how Qualytics tracks each type.

    Container Types

  • Container Attributes

    The attributes Qualytics tracks for each container, including totals, status, and last updated.

    Container Attributes

  • Complex Data Types

    How Qualytics handles nested, array, and struct types in your containers.

    Complex Data Types

Computed Assets

Computed Assets are derived datasets built from existing containers using SQL or Spark SQL transformations.

  • Computed Tables

    Derive new tables from JDBC source datastores using SQL.

    Computed Tables

  • Computed Files

    Derive new file-based datasets from DFS source datastores using Spark SQL.

    Computed Files

  • Computed Join

    Combine data from two or more containers (pairwise or with a Spark SQL query), including cross-datastore joins, into a single result.

    Computed Join

Working with Containers

  • Manage Tables and Files

    Configure settings, add checks, run operations, and manage containers across your datastores.

    Manage Tables and Files

  • Actions on Container

    The Settings, Add, and Run sections in the container context menu.

    Actions on Container

  • Data Preview

    Preview sample rows from a container before running checks against it.

    Data Preview

  • Lineage

    Track upstream and downstream relationships between containers across datastores.

    Lineage