AWS Certified Data Analytics Specialty 2021 - Hands-On! - Introduction to Kinesis Analytics

The video tutorial introduces AWS Kinesis Analytics, a tool for querying and analyzing streaming data. It covers the main components, including input data, reference tables, and real-time analytics. The tutorial explains how to use SQL for data processing and highlights the integration with AWS Lambda for enhanced data manipulation. It also introduces Kinesis Data Analytics for Apache Flink, allowing custom Flink applications to process data streams. The video concludes with practical use cases, such as ETL processes, metric generation, and responsive analytics.

What is the primary function of Kinesis Data Analytics?

• To manage AWS resources

• To query streams of data continuously

• To store large amounts of data

• To provide cloud storage solutions

Which of the following is NOT a component of Kinesis Data Analytics?

• Error stream

• Data warehouse

• Application code

• Input data sources

How does Kinesis Data Analytics handle errors during data processing?

• It retries the operation automatically

• It logs errors to a file

• It ignores the errors

• It sends errors to an error stream

What is a benefit of using reference tables in Kinesis Data Analytics?

• They are expensive to maintain

• They simplify data joins

• They require complex setup

• They are only available in SQL

What advantage does integrating Kinesis Data Analytics with AWS Lambda provide?

• Increased data storage capacity

• Enhanced post-processing flexibility

• Reduced data transfer costs

• Limited data processing options

What is Apache Flink used for in Kinesis Data Analytics?

• Creating virtual machines

• Storing data in S3

• Processing data streams

• Managing AWS resources

Which of the following is a common use case for Kinesis Data Analytics?

• Building a static website

• Creating a mobile app

• Streaming ETL processes

• Designing a database schema