AWS Certified Data Engineer – Associate Practice Pack

Explanation

Batch ingestion is the appropriate method for handling large data files that are ingested periodically, such as once every night. It involves collecting data over a period and processing it as a single batch, which is efficient for large volumes and less time-sensitive data. This contrasts with streaming ingestion, which is designed for continuous, real-time data flows. Interactive and direct query ingestion modes are more suited for real-time or on-demand queries rather than bulk file ingestion. Why the other options are incorrect: - Streaming ingestion: Streaming ingestion is optimized for real-time, continuous data flows rather than large, periodic data files, making it inefficient for nightly bulk loads. - Interactive ingestion: Interactive ingestion focuses on real-time, low-latency data loads, which is not necessary or efficient for large files ingested once daily. - Direct query ingestion: Direct query ingestion pertains to querying data on-demand without loading it, unsuitable for scenarios requiring bulk data processing after ingestion. Real-world example: A retail company collects daily sales transaction files from stores globally and uploads these large files once every night into AWS S3. They use batch ingestion to load and process this data efficiently in AWS analytics services for next-day sales reporting. Key takeaway: Use batch ingestion to efficiently process large, periodic data uploads rather than real-time or interactive ingestion methods.