AWS Glue Crawlers and Schema Discovery
AWS Glue crawlers automate the process of schema inference and catalog population in data lakes, transforming raw files into queryable tables. They connect to data stores, apply classifiers to determine data formats, and update the Glue Data Catalog. Crawlers can perform full or incremental crawls to optimize performance and reduce costs, particularly in high-throughput environments. Partition synchronization strategies, including scheduled crawlers and partition projection, ensure that newly added data is quickly registered for querying. Glue connections facilitate access to JDBC databases, streamlining the cataloging process across various data sources.
When data arrives continuously in a data lake, manually defining table schemas and registering partitions in the AWS Glue Data Catalog becomes unsustainable. AWS Glue crawlers solve this problem by automating schema inference and catalog population, turning raw files into queryable tables without human intervention.
This lesson covers three capabilities tested on the AWS Certified Data Engineer – Associate exam:
Crawler architecture and execution mechanics
Partition synchronization strategies
Connection configuration for external data sources
The running use case follows a common production pattern. JSON files land in an S3 data lake with
The following diagram illustrates the complete crawler execution flow from the S3 data lake to the queryable catalog.
Crawler architecture and execution
A Glue crawler is a managed component that connects to a data store, applies classifiers to determine the data format and infer the schema, and then writes or updates table definitions in the Glue Data Catalog.
Execution sequence
The crawler follows a deterministic sequence each time it runs:
The crawler launches either on a cron schedule, on demand via the console or API, or through an Amazon EventBridge rule that fires when new objects arrive in S3. ...