QuestionQ355

Data Ingestion and Transformation

A legal company is building a data pipeline to power an application that will handle peak traffic during business hours. The application will provide information about relevant laws and available lawyers. The legal document database will be updated one time each day.

The application must display up-to-date lawyer availability from a calendar database and provide complex full-text search of legal documents. The company wants to use AWS Glue for extract, transform, and load (ETL) processes. Lawyer availability information must be current within 5 minutes of any schedule changes.

Which solution will meet these requirements with the LEAST operational overhead?

  • A Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon RDS for the search functionality.
  • B Use AWS Step Functions to orchestrate AWS Glue jobs with time-based triggers and event-based triggers. Store processed data in Amazon S3. Use Amazon OpenSearch Service for full-text search capabilities.
  • C Use AWS Glue workflows with time-based triggers and event-based triggers. Store processed data in Amazon DynamoDB. Create a custom search solution by using AWS Lambda functions.
  • D Use Amazon EventBridge to schedule all AWS Glue jobs. Store processed data in Amazon RDS. Use Amazon Kendra for full-text search capabilities.
Explanation

AWS Glue workflows orchestrated through Step Functions with both time-based triggers (for the daily document load) and event-based triggers (for near-real-time updates) satisfy the freshness requirement with minimal custom scheduling logic. Amazon OpenSearch Service is purpose-built for complex full-text search over indexed documents stored in S3, whereas Amazon RDS is not designed for rich full-text search and a custom Lambda-based search engine or Kendra would add considerably more operational overhead. Learn more: Indexing Amazon S3 content with Amazon OpenSearch Service

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