What is a data catalog?

A data catalog is an inventory of all the data that an organization collects and processes. Regulatory requirements obligate organizations to secure and protect their data at all times, from collection to consumption. A data catalog organizes and classifies the data to support governance and data discovery. It facilitates operational efficiency through context-sharing, as everyone can quickly understand why and how a specific data set is used within an organization.

What are the benefits of a data catalog?

As an organizational tool, a data catalog streamlines searching for data and identifying what you use it for. We give some benefits below.

Fast asset discovery

A data catalog simplifies the process of identifying data, helping to increase employee productivity. You can then search for data using descriptive tags to quickly discover related data while also understanding the context and purpose of each data set. It offers a view of where data comes from, how it moves through systems, and how it gets transformed.  Data analysts can often conduct their analyses without heavily relying on IT, leading to quicker insights.

Enhanced data quality

Data catalogs require several fields that employees need to complete when a company ingests new data. When users access the catalog, their ability to read about data’s origins, transformation processes, and editing dates means they can have more confidence interacting with the information. A high degree of completeness helps to increase the ease of data governance and improve data quality. Businesses can also automate the generation of this data catalog metadata to provide comprehensive data catalogs with less effort. 

Increased efficiency

A data catalog encourages consistency in naming, definitions, and metrics, ensuring that different teams within an organization are aligned in their understanding and use of data. With visibility into all data assets, organizations can reduce data redundancy, ensuring that efforts are not duplicated and storage costs are minimized. The productivity gains that data scientists experience also help to reduce overall costs.

Enhanced security

Privacy regulations require organizations to know where personal data resides and who has accessed it. A data catalog can help in ensuring that sensitive data is handled correctly and access is granted appropriately. Organizations can track where their data comes from, who has accessed it, and how it is being used, thus enhancing regulatory compliance initiatives. 

What are the use cases of a data catalog?

Organizations can use data catalogs to streamline their storage and data management. Below are some of the use cases for a data catalog.

Self-service analytics

A data catalog provides a detailed description of what data contains and what a business uses it for. It also allows businesses to differentiate many similar pieces of data and speed up any process relating to retrieving and using data—especially in enterprise environments. This enhanced transparency allows users to quickly determine what data they are looking at and discover all necessary information in one location. You can create self-service analytics workflows for non-technical data users, even with large data volumes in storage.

Knowledge sharing

Collaboration is key to deriving actionable insights from data. A data catalog fosters a collaborative environment by allowing users to comment on, rate, and review data sets. By sharing their experiences and knowledge about specific data sets, users can work together to reduce risks and accelerate analytics throughout the organization.

Data lineage analysis

Understanding where data originates and how it traverses through various systems is critical for troubleshooting data issues, performing impact analyses, or meeting compliance standards. A data catalog provides visibility into data lineage, giving users a clear picture of data's journey from its source to its final destination. Businesses can create internal taxonomy documents allowing all employees to understand the correct names of all data assets. Having a reference document or sheet in a data catalog increases data coherence across the organization.

What information does a data catalog contain?

Data catalogs contain metadata to describe your inventory of data assets and give additional information about what data contains. Metadata fields allow you to quickly search through data and locate assets. A data catalog can include a range of metadata, such as the following examples.

Business metadata

Business metadata is any information that relates to the value it provides to a business. It could include information about the use of the data in a business, regulatory compliance details, and useful business context for other users. For example, it may contain data project annotations like data confidentiality levels, descriptions, location, users, department, and more. An organization will typically define the exact business data they need and include several related fields.

Technical metadata

Technical metadata describes the overall structure of a data set. It describe the structure of data objects, commenting on their relationships, connections, indexes, rows, columns, and tabular form. This metadata also provides context to data professionals about processes that data must undergo, such as moving through transformation or into analysis. Users rapidly understand how an organization has organized and displayed information. 

Operational metadata

Operational metadata comments on the origin of data and its transformation, updates, cardinality, and other process identification markers. Using operational metadata, you can see how the data entered your organization, what transformation it went through, and other current status updates. With operational metadata fields, you can see when users last edited data and who has permission to edit the data.

What are the key features of a data catalog?

Modern data catalog platforms use various key features to streamline their use and increase efficiency. 


Automation allows businesses to manage their data catalog with less effort. Integration capabilities allow the catalog to automatically pull metadata from various sources. The catalog remains current when new data assets are added or existing ones are updated. Some advanced systems also leverage machine learning to improve and refine their data categorization processes over time. Automation features within a data catalog enhance agility despite ever-increasing data volumes.

Efficient search options

Data catalog search features go beyond basic keyword searches to provide suggestions. They also incorporate filters so users can find the data based on various criteria. The user experience is akin to modern search engines, providing results that are relevant, ranked, and quick to access. Efficiency in data retrieval saves time while encouraging data discovery and exploration. 

Universal glossary 

A universal glossary offers standardized definitions for terms and metrics across an organization. It ensures all metadata terms have a single, clear definition. When users come across a term in the catalog, they can refer to the glossary for its meaning, ensuring consistent understanding and usage across the board. This is particularly crucial for maintaining data integrity and promoting clear communication among different teams.

What is the difference between data governance and a data catalog?

Data governance is a methodology that ensures data is in the proper condition to support business initiatives and operations. ​​Establishing the right governance means balancing data access and control and giving people trust and confidence in data while encouraging experimentation. It offers a framework that people can follow when using enterprise data and technology. Data governance is useful for ensuring a high quality of data and appropriate use under regulatory restrictions.

Data catalogs are a technology to implement data governance policies. Data governance defines data usage policies while data catalogs enforce them. These catalogs allow businesses to keep track of their data governance more effectively. 

How can AWS support your data catalog requirements?

AWS Glue is a serverless data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources for data analysis, machine learning (ML), and application development. AWS Glue Data Catalog is a central repository to store structural and operational metadata for all your data assets. You can store a given data set's table definition and physical location, add business-relevant attributes, and track how this data has changed over time.

The Data Catalog also integrates with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. Once you add your table definitions to the Data Catalog, you can have a common view of your data between these services.

AWS Glue provides numerous ways to populate metadata into the Data Catalog. For example, you can:

  • Set up AWS Glue crawlers to scan various data stores and automatically infer schemas, partition structure, and populate the Data Catalog with corresponding table definitions and statistics.
  • Schedule crawlers to run periodically so your metadata is always up to date and in sync with the underlying data. 
  • Manually add and update table details using the AWS Glue console or by calling the API. 

Get started with data catalogs on AWS by setting up a free account today.

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