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Building a Serverless Data Lake

////Building a Serverless Data Lake

Building a Serverless Data Lake

Course ID: AWS-BSDL 1 Day
   

Building a Serverless Data Lake

Overview

Building a Serverless Data Lake is a one-day, advanced-level bootcamp designed to teach you how to design, build, and operate a serverless data lake solution with AWS services. The bootcamp 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.

Description

Audience

This course is intended for:

  • Solutions architects
  • Big Data developers
  • Data architects and analysts
  • Other hands-on data analysis practitioners

Certification

Exam

Investment

Instructor-led / Virtual Instructor-led

Singapore: SGD850

PREREQUISITES

We recommend that attendees of this course have the following 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

OBJECTIVES

This course teaches you how to:

  • 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.

COURSE CONTENT

Day 1

  • 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

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