How can AWS Glue assist with schema evolution?

How can AWS Glue assist with schema evolution?

• A. It requires manual updates to the schema by the user
• B. It automatically detects changes to the schema of data sources
• C. It only supports static schemas
• D. It merges multiple schemas into one

Answer: (B)

AWS Glue is designed to facilitate the handling of data and analytics tasks in a cloud environment, including managing schema evolution. The ability to automatically detect changes to the schema of data sources is a key feature that makes a data pipeline more flexible and efficient. When working with data lakes or data warehouses, schema evolution can occur when the structure of the data changes over time due to new requirements, data source modifications, or simply the addition of new fields. AWS Glue employs a feature called the Glue Data Catalog, which works in conjunction with its ETL (Extract, Transform, Load) capabilities. This feature allows AWS Glue to monitor and recognize changes in the schema of the underlying data sources continuously. When the schema changes are detected, AWS Glue can automatically adjust its metadata catalog to reflect these changes. This capability reduces the need for manual intervention, allowing organizations to adapt to evolving data requirements without significant downtime or re-engineering efforts. In contrast, other options highlight limitations or misconceptions about the capabilities of AWS Glue. Manual updates to the schema would imply a lack of automation, contradicting the purpose of AWS Glue in this context. Static schemas suggest inflexible design, which undermines the primary advantage of using AWS Glue for dynamic and changing datasets. Merging multiple schemas into one

AWS Glue is designed to facilitate the handling of data and analytics tasks in a cloud environment, including managing schema evolution. The ability to automatically detect changes to the schema of data sources is a key feature that makes a data pipeline more flexible and efficient.

When working with data lakes or data warehouses, schema evolution can occur when the structure of the data changes over time due to new requirements, data source modifications, or simply the addition of new fields. AWS Glue employs a feature called the Glue Data Catalog, which works in conjunction with its ETL (Extract, Transform, Load) capabilities. This feature allows AWS Glue to monitor and recognize changes in the schema of the underlying data sources continuously.

When the schema changes are detected, AWS Glue can automatically adjust its metadata catalog to reflect these changes. This capability reduces the need for manual intervention, allowing organizations to adapt to evolving data requirements without significant downtime or re-engineering efforts.

In contrast, other options highlight limitations or misconceptions about the capabilities of AWS Glue. Manual updates to the schema would imply a lack of automation, contradicting the purpose of AWS Glue in this context. Static schemas suggest inflexible design, which undermines the primary advantage of using AWS Glue for dynamic and changing datasets. Merging multiple schemas into one