This video tutorial explains how to move data into AWS using three main methods: real-time, near real-time, and batch collection. Real-time collection allows immediate action on data using services like Kinesis, SQS, and IoT. Near real-time collection is more reactive, utilizing Kinesis Firehose and Database Migration Service. Batch collection is used for moving large data volumes for historical analysis, employing tools like Snowball and Data Pipeline. The tutorial provides a comprehensive overview of these methods, helping viewers understand the different approaches to data movement in AWS.

Which AWS service is used for real-time data collection to allow applications to react immediately to events?

• Kinesis Data Firehose

• Simple Queue Service (SQS)

• Snowball

• Data Pipeline

What is the primary characteristic of near real-time data collection?

• Immediate action on data

• Reactive action on data

• Batch processing of data

• Historical analysis of data

Which service is NOT typically used for near real-time data collection?

• Simple Queue Service (SQS)

• IoT

• Database Migration Service

• Kinesis Data Firehose

What is the main purpose of batch data collection in AWS?

• To move large amounts of data for historical analysis

• To enable Internet of Things (IoT) applications

• To perform immediate actions on data

• To react to data in real-time

Which AWS service is used for batch processing of large data sets?

• Snowball

• Simple Queue Service (SQS)

• Kinesis Data Streams

• IoT