AWS Glue Data Catalog: Architecture, Components, and Crawlers

March 16th, 2022

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AWS Glue is one of the most popular cloud-based ETL solutions, especially if your data infrastructure is mostly hosted on AWS. The AWS Glue Data Catalog acts as a metadata store for AWS services and any services outside of AWS that are compatible with a Hive metastore.

What is AWS Glue?

AWS Glue is a cloud-based ETL tool that allows you to store source and target metadata using the Glue Data Catalog, based on which you can write and orchestrate your ETL jobs either using Python or Spark. AWS Glue offers a great alternative to traditional ETL tools, especially when your application and data infrastructure are hosted on AWS. Glue is a fully managed ETL service that would make it easier to categorize, clean, transform, and transfer data between different data stores. These data stores could be EC2 machines, object storage, different types of databases, caches, and so on.

AWS Glue Environment

AWS Glue Environment. Source: AWS Glue Developer Guide

Use cases of AWS Glue

AWS Glue isn’t just an ETL tool; it is a lightweight job orchestrator, a data catalog solution, and much more. AWS Glue can help you

  • Extract, transform, and load data from various sources to various targets, either using out-of-the-box patterns or writing your own.
  • Run serverless queries against your data lake in Amazon S3 using Redshift Spectrum or Amazon Athena.
  • Understand your data by storing and organizing metadata in a Hive-compatible metastore using AWS Glue Data Catalog.

How does AWS Glue work?

AWS Glue depends on the AWS Glue Data Catalog, which maintains metadata about sources and targets. Glue executes ETL jobs based on this metadata to move data from sources to targets.

Data Sources supported by AWS Glue

Glue supports a wide variety of data sources, both batch-based and stream-based. Glue offers seamless support for AWS services, such as S3, RDS, Kinesis, DynamoDB, and DocumentDB. It supports any database or data warehouse as a source that can be exposed using a JDBC connection. Apart from that, AWS Glue also supports MongoDB and Apache Kafka as data sources.

Data Targets supported by AWS Glue

AWS Glue supports, more or less, all the sources as targets, too, such as Amazon S3, RDS, MongoDB, DocumentDB, and any database that can be exposed using a JDBC connection.

Understanding AWS Glue’s Architecture

AWS Glue is made up of several individual components, such as the Glue Data Catalog, Crawlers, Scheduler, and so on. AWS Glue uses jobs to orchestrate extract, transform, and load steps. Glue jobs utilize the metadata stored in the Glue Data Catalog. These jobs can run based on a schedule or run on demand. You can also run Glue jobs based on an event trigger, hence laying the foundation for event-driven ETL pipelines.

AWS Glue Data Catalog

AWS Glue uses a Hive-compatible metastore as a data catalog. The data catalog is essential to ETL operations. Before running your job, you need to catalog the source and target metadata.

Because of its Hive compatibility, the AWS Glue Data Catalog can also be used as a standalone service in combination with a non-AWS ETL tool.

AWS Glue ETL Operations

After ensuring that the data stores are crawled properly to get the latest metadata information into the AWS Glue Data Catalog, you are ready to use Glue to move data around within your data ecosystem. The data moving requires a Glue job, which uses either a Python or Spark environment to execute. Your execution speed, resources, and costs will differ depending on your choice.

AWS Glue Crawlers

Crawlers are scripts to get the latest metadata from a data store. If you are using a database as a data store, think of crawlers as running a SELECT query on the information_schema. Crawlers can either be run on a schedule or on-demand. Crawlers utilize predefined classifiers to determine the schema of your data. This schema is then replicated in the Glue Data Catalog as closely as possible. Crawlers can help you get the metadata from a variety of data stores.

AWS Glue Management Console

Like all other services, AWS Glue can be managed via multiple interfaces. The most popular and easy-to-use amongst these interfaces remains the Management Console. Using the Management Console, you can work with jobs, crawlers, classifiers, sources, targets, etc. Alternatively, you can also use the API or the SDK specific to your programming language or development framework.

Job Scheduling and Orchestration

For scheduling, AWS Glue uses cron expressions. Most advanced workflow management tools and orchestration engines like Airflow and Prefect also use some variants of cron expressions to schedule jobs. With the ability to define several types of dependencies between jobs, you can create complex ETL workflows, imitating DAGs (directed acyclic graphs), which are essential to handle real-world ETL scenarios.

What is AWS Glue Data Catalog?

AWS Glue Data Catalog is a Hive-compatible metastore used by AWS Glue as a uniform repository of metadata coming from disparate systems. In addition to being a data catalog, AWS Glue Data Catalog also offers audit and data governance capabilities.

Key features of AWS Glue Data Catalog

  • Persistent, Hive-compatible metastore for enabling ETL operations
  • IAM-based fine-grained access control for enhanced data security
  • Comprehensive audit and data governance for compliance

Use cases of AWS Glue Data Catalog

You can use the AWS Glue Data Catalog with various services in following ways:

  • Creating a Data Lake usingAWS Lake Formation— AWS Lake Formation is a fully-managed data lake solution that uses AWS Glue Data Catalog. AWS Lake Formation manages Glue ETL jobs, crawlers, and the Data Catalog to transport data from one layer of the data lake solution to another.
  • Querying External Data usingAthena & Redshift Spectrum— After storing schema information and other metadata for an external data source, you can use Athena or Redshift Spectrum to query the data source directly. The AWS Glue Data Catalog retains the schema information, which helps the query engine read data from the data source.
  • Data Processing & Analysis usingAmazon EMR— In scenarios where you have to perform process large amounts of data or complex data analysis, you can take advantage of EMR, which gives you EC2 machines that run in a Hadoop ecosystem, giving you an easy way to write and execute Spark or PySpark code.
  • Using Glue Data Catalog as a Standalone Metastore — AWS Glue Data Catalog can be used as a standalone service with any data store compatible with Apache Hive. So, even if your data infrastructure doesn’t completely lie on AWS, you can still take advantage of AWS Glue Data Catalog as your central metadata repository to enable ETL operations using a separate ETL tool.

AWS Glue Data Catalog: Benefits and limitations

There are many benefits to using AWS Glue Data Catalog because it:

  • Is Serverless — You only need to pay for what you use. No need to provision resources in advance.
  • Has Out-of-the-box Cataloging & ETL capabilities — It can enable ETL operations with built-in classifiers. Even for ETL, there are built-in transforms that you can capitalize on.
  • Offers Built-in workflow & orchestration — With Glue, you can create complex workflows that can take care of your ETL workloads. You can also use it with external orchestration tools, but the integration won’t be smooth.

Although AWS Glue Data Catalog comes with several benefits listed above, there are a few limitations too:

  • Language support — Currently, Glue supports only two programming languages and frameworks, i.e., Python and Spark (Scala and PySpark). Although these two cover a lot of ground for ETL and data analysis, other frameworks, such as Node.js, Julia, and R, need to be taken more seriously.
  • Closed-source — Like many other services, Glue remains a black box for developers, making it difficult to innovate and extend Glue’s capabilities.

Using Crawlers to populate AWS Glue Data Catalog

What are AWS Glue crawlers?

You need AWS Glue Data Catalog to have the metadata information for source and target schemas to perform ETL operations. AWS Glue crawlers are scheduled or on-demand jobs that can query any given data store to extract scheme information and store the metadata in the AWS Glue Data Catalog. Glue Crawlers use classifiers to specify the data source you want it to crawl.

General workflow of how crawlers populate AWS Glue Data Catalog

A Glue Crawler gets the metadata from a data source and writes to the AWS Glue Data Catalog in the following manner:

  • Glue uses a built-in or custom classifier to determine the data's format, schema, and other properties. In SQL terms, imaging this being a SELECT query on a sample of the actual data and approximating the table's structure based on the sample.
  • Glue Crawler groups the data into tables or partitions based on data classification. If the crawler is getting metadata from S3, it will look for folder-based partitions so that the data can be grouped aptly.
  • Glue pushes the data into the AWS Glue Data Catalog, after which the crawled datastore is ready to be used in ETL operations.

AWS Glue Data Catalog

Work flow diagram populating AWS Glue Data Catalog. Source: AWS Glue Developer Guide

Glue offers a variety of classifiers that cover most of the popular data stores. However, if you don’t find your data source covered by the built-in classifiers, you can write your own classifier to crawl the data store.

AWS Glue vs. EMR

AWS Glue and EMR both provide data computation and processing services. Both these services overlap in many of the features while offering something unique also because of the way they’ve been implemented. AWS Glue is a serverless ETL service, while AWS EMR uses EC2 instance clusters to create a Hadoop ecosystem for processing large amounts of data.

When you don’t know your data processing requirements, it’s better to use AWS Glue as it has a pay-as-you-go model. With EMR, you’d have to dedicate money to infrastructure that you may not even end up using. Moreover, EMR only solves the computation and processing problem; it doesn’t offer any data cataloging or workflow orchestration service. AWS Glue has the edge over EMR on this too.

EMR would make sense for you to use if you have a very good idea about the volume of data you’ll be processing, along with the type and complexity of the transforms you’ll be performing on that data.


All in all, AWS Glue Data Catalog is a popular tool that can be independently used, although it does come with some limitations we discussed above. While it provides a pay-as-you-go pay structure because of its serverless nature, it also lacks integration capabilities with non-AWS services. With many companies going the multi-cloud route, this can be challenging.

Having said that, if you have enough technical expertise and data stores with Hive compatibility, you can make the most out of AWS Glue Data Catalog, even without using a lot of other AWS services. On the other hand, if your data infrastructure is completely hosted on AWS, Glue can be a very powerful and time-saving integration that you can use.

Evaluating data catalogs? You will need to identify and evaluate which of the available solutions are best suited to your data teams unique needs and complexities

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