Course Description

In this one-day, advanced course, you will learn to design, build, and operate a serverless data lake solution with AWS services. This course will include topics such as ingesting data from any data source at large scale, storing the data securely and durably, enabling the capability to use the right tool to process large volumes of data, and understanding the options available for analyzing the data in near-real time.

Objectives

• Collect large amounts of data using services such as Kinesis Streams and Firehose and store the data durably and securely in Amazon Simple Storage Service.
• Create a metadata index of your data lake.
• Choose the best tools for ingesting, storing, processing, and analyzing your data in the lake.
• Apply the knowledge to hands-on labs that provide practical experience with building an end-to-end solution.
• Configure Amazon Simple Notification Service (Amazon SNS) to audit, monitor, and receive event notifications about activities in the data warehouse
• Prepare for operational tasks, such as resizing Amazon Redshift clusters and using snapshots to back up and restore clusters
• Use a business intelligence (BI) application to perform data analysis and visualization tasks against your data

Audience

• Solutions architects
• Big Data developers
• Data architects and analysts
• Data analysis practitioners

Prerequisites

• Good working knowledge of AWS core services, including Amazon Elastic Compute Cloud (EC2) and Amazon Simple Storage Service (S3)
• Some experience working with a programming or scripting language
• Familiarity with the Linux operating system and command line interface
• Requires a laptop to complete lab exercises – tablets are not appropriate

Content

This course covers the following concepts:

• Key services that help enable a serverless data lake architecture
• A data analytics solution that follows the ingest, store, process, and analyze workflow
• Repeatable template deployment for implementing a data lake solution
• Building a metadata index and enabling search capability
• Setup of a large-scale data-ingestion pipeline from multiple data sources
• Transformation of data with simple functions that are event triggered
• Data processing by choosing the best tools and services for the use case
• Options available to better analyze the processed data
• Best practices for deployment and operations