Welcome back! In the previous lesson, you learned how to create and organize AWS Glue databases, which act as logical containers for your data lake’s metadata. Now that you have a Glue database set up, it’s time to take the next step: connecting Glue to your actual S3 data and automatically discovering the structure of your datasets. This is a key part of making your data lake truly usable, as it allows you to search, analyze, and process your data with other AWS services.

In this lesson, you will learn how to use AWS Glue Crawlers to scan your S3 data, detect its schema, and create tables in your Glue Data Catalog. By the end, you will know how to automate the process of cataloging new data as it arrives in your data lake, making your data easily accessible for analytics and ETL jobs.

Before we dive into the code, let’s briefly review what a Glue crawler is and how it fits into the Glue Data Catalog. You already know that a Glue database is a container for tables, but how do those tables get created and kept up to date? That’s where crawlers come in.

A Glue crawler is a tool that connects to your data sources — like S3 buckets — and automatically scans the files it finds. It examines the data, infers the schema (such as column names and data types), and then creates or updates tables in your Glue database. This process is essential for keeping your data catalog accurate and up to date, especially as new data lands in your S3 data lake.

When you run a crawler, it will look at the files in the specified S3 path, figure out the structure, and register that information as tables in your Glue database. These tables are not the data itself, but rather metadata that describes where the data is stored and how it is structured. This makes it possible for tools like AWS Athena to query your data directly from S3.

Let’s walk through a complete example of how to connect Glue to your S3 data, run a crawler, and inspect the results. The following Python script uses the boto3 library to automate the process. If you are using the CodeSignal platform, boto3 is already installed for you. If you are working on your own device, make sure you have installed boto3 and configured your AWS credentials.

The script below will:

• Create a Glue crawler that targets your S3 bucket and path.
• Start the crawler (without waiting for it to finish).
• List the tables discovered by the crawler, including their columns and partition keys.

As you work through this process, you may encounter a few common issues. If you try to create a crawler that already exists, the script will let you know and continue without failing. This is intentional and helps make your workflow repeatable and safe.

Another important aspect is permissions. For the crawler to access your S3 data and update the Glue Data Catalog, it needs to run with an IAM role that has the right permissions. In the example above, the script uses a role called AWSGlueServiceRole. This role must exist in your AWS account and have policies that allow access to both Glue and the S3 bucket you are cataloging. If you are using CodeSignal, these permissions are already set up for you. If you are working in your own AWS account, make sure the role exists and has the necessary permissions.

If you see errors related to permissions or missing roles, double-check your IAM setup. Most issues can be resolved by ensuring the correct role is specified and that it has the required access to Glue and S3.

In this lesson, you learned how to connect AWS Glue to your S3 data lake, use a crawler to automatically discover and catalog your data, and inspect the resulting tables and schema in your Glue Data Catalog. This process is essential for making your data lake usable and ready for analytics, as it allows you to keep your metadata up to date as new data arrives.

You are now ready to put these skills into practice. In the next set of exercises, you will create and run your own Glue crawler, explore the tables it discovers, and troubleshoot any issues you encounter. This hands-on experience will help you build confidence in managing your data lake and prepare you for more advanced data processing tasks. Keep going — your data lake is becoming more powerful and organized with each step!