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Containers Overview

Containers are fundamental entities representing structured data sets. These containers could manifest as tables in JDBC datastores or as files within DFS datastores. They play a pivotal role in data organization, profiling, and quality checks within the Qualytics application.

Types of containers

There are two main types of containers in Qualytics:

  • JDBC containers: These containers represent tables, views, and other objects in a relational database.
  • DFS containers: These containers represent files, such as CSV, JSON, and parquet files, typically stored in distributed file systems like Hadoop or cloud storage.

Container Attributes Reference

The following table outlines the key attributes of a container and their significance:

Attribute Description
Freshness Indicates the recency of the data within the container. Not applicable to computed tables.
Service Level Agreement (SLA) Reflects compliance with defined performance metrics and thresholds.
Timestamps Timestamp of the last profiled operation and the last scanned operation on the container.
Tags Metadata labels assigned to the container for identification and categorization purposes.
Incremental Strategy The strategy employed to track and apply row-level changes for incremental profiling and scanning.
Quality Score & Completeness Metrics that represent the integrity and usability of the data within the container.
Records & Fields The count of total records and the structure of fields, including columns or headers, within the container.
Checks & Anomalies The total number of quality checks performed and anomalies detected within the container.

Actions on Containers

Users can perform various operations on containers:

  1. Settings: Configure incremental strategy, partitioning fields, and exclude specific fields from analysis.
  2. Quality Checks: Add authored quality checks tailored to the container.
  3. Operate: Execute profiling and scanning operations.
  4. Freshness: Schedule and manage data freshness checks, applicable only to certain container types.
  5. Export: Export quality checks and field profiles to an enrichment datastore for further action or analysis.
  6. Deletion and Recreation: Containers can be deleted with the option to be recreated if necessary.

Field Profiles

After profiling a container, individual field profiles offer granular insights:

  • Type: Classifies the data type of the field (e.g., String, Fractional, Integral).
  • Tags: Attached tags.
  • Quality Score: Quantitative measure of data quality for the field.
  • Completeness: Indicates the presence of all expected data within the field.
  • Quality Checks: Number of quality checks associated with the field.
  • Anomalies: Count and details of any anomalies detected in the field.

Last update: June 14, 2024