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Data Catalogs

Organize, manage and store metadata information

Data catalogs are crucial in the data engineering process as they allow to organize, manage and store metadata information about data sources, making it easier to discover and understand the data assets.
5 minutes 5 Questions

Data Catalogs serve as comprehensive metadata repositories that enable organizations to efficiently manage, discover, and understand their data assets across distributed environments. Acting as a centralized inventory system, a data catalog indexes and organizes metadata about datasets, including t…

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Big Data Engineer - Data Catalogs Example Questions

Test your knowledge of Data Catalogs

Question 1

What is data lineage in relation to a data catalog?

Correct Answer: Data lineage is a record of the origin and movement of a piece of data throughout the data pipeline.

Data lineage is important in understanding how data is transformed and used throughout the data pipeline. It helps with debugging and identifying potential issues in the data pipeline.

Question 2

What is the difference between a metadata repository and a data catalog?

Correct Answer: A metadata repository is a database that stores metadata about data assets, whereas a data catalog is an interface for browsing and searching metadata about data assets.

A metadata repository is a centralized database for storing metadata about data assets, such as data lineage, data quality, and data usage. In contrast, a data catalog is an interface for browsing and searching metadata about data assets. While some data catalogs may store metadata in a repository or database, the focus of a data catalog is on providing an interface that makes it easier for users to discover, access, and analyze data.

Question 3

What is data catalog curation?

Correct Answer: It is a process of creating, updating, and maintaining metadata in a data catalog.

Data catalog curation involves the creation, updating, and maintenance of metadata in a data catalog. Sometimes referred to as data catalog management, it involves a broad range of activities such as metadata discovery, data lineage, metadata tagging, and schema mapping.

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