A comparison between Amazon Kinesis and DynamoDB Streams
Tags: architecture, aws, cloudskills, devops
Published: 2021-09-05 12:33:19

![Amazon Kinesis - Process and Analyze of streaming Data - AWS]
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<figcaption>Amazon Kinesis - Process and Analyze of streaming Data - AWS

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Source: https://aws.amazon.com/kinesis/
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I wrote this article with AWS certification tests in mind, and if you're studying for your AWS certification and wondering what the difference between DynamoDB Streams and Amazon Kinesis is, then read on.

Since services come up all the time on the certification tests, you'll need to have a thorough comprehension of them to do well. But what are Amazon Kinesis anyway and how it differs from DynamoDB streams? 

Amazon Kinesis is a scalable collection of services for processing streams of various data that provides a way to easily collect, process and analyse real-time data. Kinesis Data Streams applications typically read data from a data stream and save it as a data record. These applications can use the Kinesis Client Library, and they can run on Amazon **EC2 instances**. For businesses, this is extremely useful since they receive timely insights from the well-processed Kinesis data, react quickly to new information, and make better and faster decisions. 

We'll talk about four types of Kinesis services in this article, so let's get started:-

1. **Video Streams:** AWS Video Streams is a service for securely uploading videos in real-time for *Analytics, Machine Learning, Video Processing, and Video Playback.* By using Kinesis Video Streams, all the infrastructure required to ingest streaming video data from millions of devices is automatically provisioned and scaled, so you need not worry about the configuration of your environment.

2. **Data Streams:** Kinesis Data Streams *(KDS)* is part of the Kinesis streaming data platform. KDS is massively scalable and reliable, capable of capturing gigabytes of data per second from hundreds of thousands of sources such as database event streams, financial transactions, location-tracking events and social media feeds. Data collected by KDS is available in milliseconds, which can then be used for real-time analytics. Dashboards, dynamic pricing, real-time anomaly detection, and more are examples of use cases. 

3. **Data Firehouse:** Another fully managed service for delivering real-time streaming data to destinations like Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES). In addition, Firehose can batch, compress, transform, and encrypt your data streams before they are even loaded, reducing storage requirements while increasing security at the same time. 

4. **Kinesis Data Analytics:** This service enables you to quickly write SQL code that continuously reads processes, and stores data in near real-time. It is a simple way to transform and analyze streaming data with **Apache Flink** *(an open-source framework for processing data streams).* The integration of Apache Flink with other AWS services is also simplified with Kinesis Data Analytics.

Stay tuned next week as we'll go over DynamoDB Streams. 🤞🏽