Discovering Data with AWS Glue Crawlers

1. What is an AWS Glue Crawler?

In the simplest terms, a Glue Crawler is an automated program that connects to your data stores (like Amazon S3 or a relational database), scans your data, and automatically determines its schema. It then creates or updates tables in your Glue Data Catalog with this discovered metadata.

Think of it as a librarian who can read every book in a new shipment, figure out its genre, title, and author, and create a perfectly organized card catalog entry for it, all without you having to provide any instructions beyond pointing them to the shipment.

The workflow is as follows:

A crawler's core process involves a few key steps:

1. Connecting to the data source you specify.
2. Classifying the data to determine its format (e.g., CSV, JSON, Parquet) and inferring its schema (column names and data types).
3. Grouping similar data into tables.
4. Writing the metadata to the Glue Data Catalog.

2. How a Crawler Works: Classifiers and Configuration

The "magic" of a crawler lies in its use of classifiers. When a crawler scans your data, it uses an ordered list of classifiers to try and understand the data's format and schema.

There are two types of classifiers:

• Built-in Classifiers: AWS Glue provides these for common file types like CSV, JSON, Parquet, Avro, and various log formats. The crawler will try these in a specific order.
• Custom Classifiers: You can create your own classifiers to handle non-standard or proprietary data formats that the built-in classifiers can't recognize. We'll cover this in our next lesson.

A classifier returns a "certainty" score. If it's 100% certain it recognized the format (certainty = 1.0), the crawler uses that classification. If not, it moves to the next classifier in the list.

3. Creating and Configuring a Glue Crawler

Let's get practical. Creating a crawler involves guiding it through a wizard in the AWS Management Console. We'll walk through the main steps.

The official AWS documentation provides a tutorial with a public S3 bucket, which is perfect for a hands-on exercise.

To complement the text guide, watching a video demonstration can clarify each configuration step.

Let's summarize the key configuration choices you just saw:

• Crawler source type: Usually Data stores for sources like S3.
• Data store: The S3 path, JDBC connection, or DynamoDB table to scan.
• IAM Role: This is a critical security component. The crawler needs permissions to s3:GetObject and s3:ListBucket for the data source, and glue:* permissions to create and update the Data Catalog. The console can conveniently create a role with the necessary policies for you.
• Schedule: You can run it On demand or on a recurring schedule (hourly, daily, or using a cron expression).
• Output Database: The Glue database where the discovered table(s) will be created.
• Grouping behavior for S3 data: If you have multiple files in a folder with slightly different schemas, this option tells the crawler whether to create one table (by merging schemas) or multiple tables. Create a single schema for each S3 path is the most common setting to avoid an explosion of tables.
• Crawler update/delete behavior: You define how the crawler handles changes:
  • Schema Changes: Should it update the table definition, add new columns only, or ignore changes?
  • Deleted Objects: Should it delete the table, mark it as deprecated, or do nothing if the source data disappears?

4. Running the Crawler and Managing Schema Evolution

Once your crawler is created, you run it. The crawler will then perform its scan and, upon completion, report how many tables were created or updated.

The real power of crawlers becomes apparent when managing a dynamic data lake where data and schemas change. This is known as schema drift or schema evolution.

This ability to automatically adapt to schema changes makes crawlers an indispensable tool for maintaining an accurate and up-to-date Data Catalog with minimal manual intervention.

Conclusion

Today you've learned how to automate one of the most critical tasks in managing a data lake: cataloging your data. By using AWS Glue Crawlers, you can move away from manual, error-prone schema definition and embrace an automated, scalable approach.

Key Takeaways:

• Glue Crawlers automate the discovery of schemas in your data stores and populate the Glue Data Catalog.
• They use classifiers (built-in or custom) to recognize data formats like CSV, JSON, and Parquet.
• Key configurations include the data source, IAM role, schedule, and output behavior.
• Properly configuring how a crawler handles schema evolution and deleted objects is crucial for maintaining a robust data pipeline.
• Running a crawler results in one or more tables being created or updated in the Data Catalog, making your raw data instantly queryable by services like Amazon Athena.

Next Up:
We've seen how well crawlers work with standard formats. But what happens when you have a custom application log or a proprietary file format that Glue doesn't recognize out of the box? In our next lesson, we will learn how to implement custom classifiers for specialized data formats, effectively teaching the crawler to understand your unique data.