What it Does (Features)

A data catalog customarily has the following features.

1. Collects and Organizes All Metadata

The first step for building a data catalog is collecting the data’s metadata. Data catalogs use metadata to identify the data tables, files, and databases. The catalog crawls the company’s databases and brings the metadata (not the actual data) to the data catalog.

A data catalog can typically crawl:,

Data Management Platforms

  • Relational Databases – Oracle, SQL Server, MySQL, DB2, etc.
  • Data Warehouses – Teradata, Vertica etc.
  • Object Storage
  • Cloud Platforms – Google Big Query, MS Azure Data Lake, AWS – Athena & Red Shift
  • Non-Relational / NoSQL Databases- Cassandra, MongoDB
  • Hadoop Distributions

Analytics and Business Intelligence Platforms

  • Modern Business Intelligence Platforms
  • Analytic Applications

Custom Applications

2. Shows Data Profile

By looking at the profile of data consumers view and understand the data quickly. These profiles are informative summaries that explain the data.

For example, the profile of a database often includes the number of tables, files, row counts, etc.. For a table, the profile may include column description, top values in a column, null count of a column, distinct count, maximum value, minimum value and much more.

3. Shows Data Lineage

Data Lineage is a visual representation of where the data is coming from, where it moves and what transformations it undergoes over time. It provides the ability to track, manage and view the data transformation along its path from source to destination.

Hence, it enables the analyst to trace errors back to the root cause in the analytics.

4. Shows Relationships Among Data

Through this feature, data consumers can discover related data across multiple databases. For example, an analyst may need consolidated customer information. Through the data catalog, she finds that five files in five different systems have customer data. With a data catalog and the help of IT, one can have an experimental area where you can join all the data and clean it. Then one can use that consolidated customer data to achieve your business goals.

5. Houses a Business Glossary
6. Tags Data Through AI

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