AWS Kinesis Firehose tutorial using Python

This video shows you Amazon Kinesis Firehose Overview. This is handy because the output of a Kinesis Analytics application writes to a data stream (which means the output of Kinesis Analytics can be sent to a Lambda function via a stream).

We will drive you through the configuration of different cloud services such as Kinesis Stream, Lambda functions, and StepFunctions, to implement a data collection pipeline.

The tutorial illustrates a real-world example of how to collect data from your web server, mobile client and cloud application, and forward them to third-party services and tools or load them into your data warehouse. If you’re interested in serverless computing, we recommend the Serverless Computing on AWS

Amazon Kinesis is a fully managed, cloud-based service for real-time data processing over large, distributed data streams. AWS Lambda is a compute service that runs your code in response to events and automatically manages the compute resources for you. AWS Lambda can run code in response to data in Amazon Kinesis streams, making it easy to build big data applications that respond quickly to new information. In this webinar, we will cover key Kinesis and Lambda features, walk through sample use cases for stream processing, and discuss best practices on using the services together. We’ll then demonstrate setting up an Amazon Kinesis stream and an associated Lambda function to capture and perform custom computations on click-stream data, all without setting up any infrastructure.

Learning Objectives: • Understand key Amazon Kinesis and AWS Lambda features • Learn how to setup streaming data capture and processing framework using AWS Lambda • Learn sample use cases, best practices and tips on using AWS Lambda with Amazon Kinesis

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