AWS Glue Crawlers are automated data discovery tools that scan a data source to classify, group, and catalog the data within it automatically1. They create or update tables in your AWS Glue Data Catalog2.

Benefits

1. Automated Data Discovery: Crawlers can automatically discover both structured and semi-structured data stored in various data stores3.
2. Data Cataloging: Upon completion, the crawler creates or updates one or more tables in your Data Catalog2.
3. Multiple Data Stores: A crawler can crawl multiple data stores in a single run2.
4. Integration with Other AWS Services: The metadata created by the crawler allows services such as Athena to view the S3 information as a database with tables1.

Strengths and Weaknesses

Strengths:

• Serverless: You don’t have to build or maintain infrastructure4.
• Automation: Crawlers automate the process of data discovery and cataloging1.

Weaknesses:

• If your data is not partitioned or you don’t want to use the predicate-pushdown feature then you don’t need to run a Crawler5.
• The crawler doesn’t read files that were read in the previous crawler run6.

Use Cases

1. Data Analysis: Run queries on S3, on-premises data centers, or on other clouds7.
2. Machine Learning: Prepare data for machine learning models7.
3. Complex Tasks Simplification: Use machine learning models in SQL queries or Python to simplify complex tasks, such as anomaly detection, customer cohort analysis, and sales predictions7.

Limitations

1. Case Sensitivity: The crawler’s fields are case-sensitive8.
2. Limited Data Stores: A crawler can crawl only catalog tables in a single run; it can’t mix in other source types8.