- [Raf] If you have heard all the buzz about data lakes, big data technologies, and AWS services, but are not quite sure where to get started, you came to the right place. My name is Rafael Lopes, and I'm a Senior Cloud Technologist with AWS Training and Certification. In this class, Introduction to Designing Data Lakes in AWS, we will help you understand how to create and operate a data lake in a secure and scalable way without previous knowledge of data science. Starting with the why you may want a data lake, we will look at data lake value proposition characteristics and components and how it compares to other data scenarios, such as databases and data warehouses. During the first week, we will talk about the core knowledge needed to understand the world of data lakes. Understanding these core aspects is not only beneficial for operating AWS services, but will help you learn the fundamental components that can be instrumental everywhere else in your learning data journey. Once you have a solid understanding of what a data lake is and why a data lake may be the right solution for your needs, we will explore the AWS data-related services that will make it happen. During the second week of the course, we will begin to talk about the AWS services that can be used in data lake architectures, like Amazon S3, AWS Glue, Amazon Athena, Amazon Elasticsearch Service, Lake Formation, Amazon Rekognition, API Gateway, and other services used for data movement, processing, and visualization. During week three, we are going to ingest the rivers. In other words, we will begin to explore how to ingest data by talking about data cataloging, batch, and streaming data ingestion, and what are the AWS services used it for doing it. Also on week three, we will dive deeper into the specifics of data cataloging and ingestion by exploring services like AWS Transfer Family, Amazon Kinesis Data Streams, Kinesis Firehose, Kinesis Analytics, Kinesis Video Streams, AWS Snow Family, AWS Glue Crawlers, and others. We will also talk about when is the right time to process data. Is it before, after, or while data is being ingested? You will be able to easily identify scenarios regarding when to process data and match the most appropriate AWS services according to the specific scenarios. In week four, we are going to dive deeper into data optimization and data processing, where you will see demos around best practices showing how to optimize your data sets for performance and cost just by using the right tool for the job. Also in week four, we will cover data security, data visualization tools, AWS data sets you can explore to experiment and get started. All that with hands-on activities. Thanks for joining this four-week course. I hope you are as excited to begin this class as me and Morgan were to create it. Let's get started.