The data lake should be flexible and meet the following requirements:✑ Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum?

Explanations:

Using an AWS Glue crawler to crawl S3 data and an AWS Lambda function to trigger an AWS Glue ETL job is ideal for event-driven ETL pipelines. The AWS Glue Data Catalog provides a way to easily search and discover metadata, and both Amazon Athena and Amazon Redshift Spectrum can query data using the Glue Data Catalog.

While an AWS Glue crawler can crawl the S3 data, using AWS Lambda to trigger an AWS Batch job is less suited for event-driven ETL pipelines in this scenario. Additionally, using an external Apache Hive metastore adds unnecessary complexity for metadata management, as the AWS Glue Data Catalog should be used.

Although AWS Glue crawler and AWS CloudWatch alarm are good options, using AWS Batch for ETL jobs is not ideal in this case. AWS Glue ETL jobs would be a better fit for event-driven pipelines. Furthermore, AWS Glue Data Catalog is preferable for metadata management, not an external Apache Hive metastore.

An AWS CloudWatch alarm triggering an AWS Glue ETL job is unnecessary for this specific event-driven ETL requirement. Additionally, using an external Apache Hive metastore for metadata discovery is unnecessary, as the AWS Glue Data Catalog is better suited for this use case.