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

The video tutorial provides an in-depth look at AWS Kinesis Analytics, a tool for processing streaming data. It covers the main components, including input data sources, real-time analytics using SQL, and output streams. The tutorial also discusses error handling, the use of reference tables for data enrichment, and integration with AWS Lambda for flexible data processing. Additionally, it introduces Kinesis Data Analytics for Apache Flink, highlighting its serverless nature and use cases like ETL and continuous metric generation.

What is the primary function of Kinesis Data Analytics?

• To provide cloud storage solutions

• To manage AWS resources

• To store large amounts of data

• To query streams of data continuously

Which of the following can be used as an input source for Kinesis Data Analytics?

• AWS Lambda

• Amazon EC2

• Amazon S3

• Kinesis Data Streams

How can reference tables be utilized in Kinesis Data Analytics?

• For managing user access

• For creating virtual machines

• For enriching input data streams

• For storing data permanently

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

• Faster data retrieval

• Increased storage capacity

• Enhanced data processing flexibility

• Improved network security

What is Apache Flink used for in Kinesis Data Analytics?

• To store data in a database

• To process data streams

• To create virtual networks

• To manage cloud infrastructure

Which API is used by Flink applications in Kinesis Data Analytics?

• Storage API

• Compute API

• Data Stream API

• Network API

What is a common use case for Kinesis Analytics?

• Streaming ETL processes

• Designing a database schema

• Creating a mobile app

• Building a static website