AWS Glue - Data Catalog

What is Data Catalog?

The AWS Glue Data Catalog is central repository that stores the metadata information about your data. In simple terms, a data catalog is just like a data dictionary that keeps the details like structure of data, location of data, and how to access it using queries. This metadata information is very important to manage and organize large amounts of data.

You can store the data either in Amazon S3, Redshift, or at any other location in AWS. The main role of AWS Glue Data Catalog is to centralize the management of data and make it accessible for analysis.

Key Features of Data Catalog

Listed below are some of the key features of AWS Glue Data Catalog −

• Automatic Data Detection − AWS Glue Crawlers scan your data sources, identify the schema, and automatically catalog the metadata. This data is stored in the AWS Glue Data Catalog.

• Centralized Metadata Management − One of the key features of Data Catalog is that it centralizes all metadata in one place. Thats the reason the user need not to manually define the data. It also makes it easier to manage large data environments.

• Integration with AWS Services − The AWS Glue Data Catalog can easily integrate with AWS services like Amazon Athena, Redshift, and SageMaker. This integration allows the user to run queries or build ML models without manually handling the data.

How to Use the AWS Glue Data Catalog?

Using AWS Glue Data Catalog is very simple. First, you need to create a database which will store the metadata for your datasets. We discussed the method to create a database in the previous section.

Once you have the database, you need to create an AWS Glue Crawler which will automatically scan your sources. The crawler identifies the data structure and updates the Data Catalog with metadata like table names, columns, and data types. This metadata is then available for querying with tools like Amazon Athena.

Managing Metadata with the Glue Data Catalog

You have the metadata, which is available for querying, but effective management of this metadata is important for organizations that deal with large amounts of data. Before learning the ways to manage metadata, it is important to understand this metadata.

Understanding Metadata

Metadata is data about data. It provides the following important information such as −

• Schema − It represents the structure of your datasets. It includes tables, columns, and data types.

• Location − As the name implies, it is the AWS place where your data is stored. It can be Amazon S3 buckets or databases like Amazon Redshift.

• Description − It provides some additional information about data. It may include its purpose and the source from which it originates.

Ways to Manage Metadata

Here are some ways with the help of which you can manage metadata −

Manually Edit the Metadata

Although automatic data detection by AWS Crawlers is enough but you can also edit your data manually. To edit metadata manually, first find your databases and tables listed in the Data Catalog. Now you can click on a particular table you want to edit. You can edit its properties, columns and data type.

Edit the Metadata Using Tags

Tags are helpful for organizing and managing metadata more effectively. You can tag databases and tables with key-value pairs to categorize them easily.

Tags also enhance the searchability of your metadata which further make it easier to locate specific datasets within large collections.