Building a Serverless Data Lake on AWS


Course Number: AWS-118

Duration: 1 day (6.5 hours)

Format: Live, hands-on

AWS Training Overview

This Building a Serverless Data Lake on AWS training course is a one-day, advanced-level bootcamp designed to teach attendees how to design, build, and operate a serverless data lake solution with AWS services. Attendees learn how to ingest data from any data source at large scale, store the data securely and durably, enable the capability to use the right tool to process large volumes of data, and understand the options available for analyzing the data in near real-time.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, online classes for individuals. See a schedule of online courses.

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

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

Outline

Introduction
Key services that help enable a serverless data lake architecture
A data analytics solution that follows the ingest, store, process, and analyzes 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
Conclusion

Training Materials:

All AWS students will receive comprehensive courseware.

Software Requirements:

A system free of restrictive firewalls that prevent SSH into AWS virtual machines.



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