Processing streaming data can be complex in traditional, server-based architectures, especially if you need to react in real-time. Amazon Kinesis makes it possible to collect, process, and analyze this data at scale, and AWS Lambda can make it easier to develop highly scalable, custom workloads to turn the data into useful insights. This tech talk explains common streaming data scenarios, when to use Kinesis or Kinesis Data Firehose, and how to use Lambda in a streaming architecture. Learn about the extract, transform, load (ETL) process using Lambda and how you can implement AWS services to build data analytics. This talk also discusses best practices to help you build efficient and effective streaming solutions.

Learning Objectives:
*Understand how to manage streaming data workloads
*Learn how to use AWS Lambda to process streaming data
*Use best practices in your Lambda architectures to reduce cost and improve scale

***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/serverless/

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