Most frequently asked Kinesis Interview Questions

1. What is AWS Kinesis?
2. What are the benefits of AWS Kinesis?
3. What are the capabilities of Amazon Kinesis?
4. What are the key features of Kinesis?
5. What are the Core Services of Kinesis?
6. What is a shard in Kinesis stream?
7. What is Data Pipeline in Kinesis?
8. How to Set Up Data Pipeline?
9. How to Delete a Pipeline?
10. Name the components of Kinesis?
11. What is Machine Learning in Kinesis?
12. What are the advantages Amazon Machine Learning?

What is AWS Kinesis?

Amazon Kinesis is a managed services used for collecting the large streams of data records which runs on AWS EC2 instances.It is used for collecting, processing and analyzing the data, so we can get perfect insights as well as quick respones with respect the information.

What are the benefits of AWS Kinesis?

• Real Time - It is used for delivering real time data processing in a reliable and flexible manner.
• Easy to use - It can be easily placed in the kinesis stream with the help of Kinesis Client Library.
• Elastic - It can easily scale up from Megabytes to Terabytes.
• Low Cost - It has no upfront cost and the payment can be done only for the resources that can be used.
• Fully Managed - It is fully managed and can be easily run by all application without any need of infrastructure.
• Scalable - It is very easy for handling all the amount of streamed data with hundreds of sources with low latency.

What are the capabilities of Amazon Kinesis?

• Amazon Kinesis video streams
• They are used for securing all the stream data like videos, photos and the connected devices to the AWS for machine learning.
• Amazon Kinesis Data Streams
• They are specifically used in building real time custom model applications.It can easily ingest all the stored data with data streaming prices.
• Kinesis Data Firehouse
• They help in loading and transforming the data streams into respective data streams, it is also used for storing AWS data store near all analytics with all intelligence tools.
• Kinesis Data Analytics
• Helps in processing all real time techniques with SQL for learning all programming languages with processing frameworks.

What are the Core Services of Kinesis?

What is a shard in Kinesis stream?

Shard is a unique group of data records in stream and we can also say that it is a partition in Kinesis stream, it supports a fixed bandwidth fixed number of messages per second.It is the base throughput unit of an Amazon Kinesis data stream, one shard can provides capacity of 1MB/sec data input and 2MB/sec data output.

What is Data Pipeline in Kinesis?

Data Pipeline is designed for users to integrate data spread across multiple AWS services and analyzing from a single location.It can be accessed from the source, process, and the results can be efficiently transferred to the respective AWS services.

How to Set Up Data Pipeline?

We can create the Pipeline using the following steps:
Sign-in to AWS account
Use this link to Open AWS Data Pipeline console
Select the region in the navigation bar
Click the Create New Pipeline button
Fill the required details in the respective fields

How to Delete a Pipeline?

We can delete the Pipeline using the following steps:
Select the pipeline from the pipelines list
Click the Actions button and then choose Delete




A confirmation prompt window opens. Click Delete.

Name the components of Kinesis?

There are 12 types of components of Kinesis:

• Kinesis Data Stream
• Data Record
• Retention Period
• Producer
• Consumer
• Shard
• Partition Key
• Sequence Number
• Note
• Kinesis Client Library
• Application Name
• Server-Side Encryption

What is Machine Learning in Kinesis?

Machine Learning allows us for developing predictive application by using algorithms and mathematical models based on yhe users data, it reads data through AWS Management Console and the AWS Machine Learning API.Data can be imported and exported to other AWS services through S3 buckets.

What are the advantages Amazon Machine Learning?

• Easy for creating machine learning models - It is easy for creating ML models from data stored in Amazon S3.
• High performance - It can be used for generating billions of predictions for the application.
• Cost efficient - We can pay only for what we've used without any setup charges.