Length
1 day

Overview

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.

Please note: students are required to bring their own laptop/tablet and power cable for this course.

Key Topics

Detailed Info
  • Key Services
  • Repeatable Template Deployment
  • Setup of a Large-Scale Data Ingestion Pipeline
  • Data Processing
  • Best Practices for Deployment and Operations
  • Data Analytics Solution
  • Building of a Metadata Index
  • Transformation of Data with Simple Functions
  • Options for Analyzing the Processed Data
Skills Gained
Key Topics
Target Audience
Prerequisites

Skills Gained

In this course, participants will learn how to:

  • Collect large amounts of data using services such as Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose and store the data durably and securely in Amazon Simple Storage Service (Amazon S3)
  • 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

Key Topics

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 of 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 for analyzing the processed data
  • Best practices for deployment and operations

Please note: This is an emerging technology course. Course outline is subject to change as needed.

Target Audience

This course is intended for:

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

Prerequisites

We recommend that attendees of this course have the following prerequisites:

  • Working knowledge of AWS core services, including Amazon Elastic Compute Cloud (Amazon EC2) and Amazon S3
  • Experience working with a programming or scripting language
  • Familiarity with the Linux operating system and command line interface
  • A laptop to complete lab exercises; tablets are not appropriate

We can also deliver and customise this training course for larger groups – saving your organisation time, money and resources. For more information, please call us at +632 8244 2098 or email [email protected]

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The supply of this course by DDLS is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions

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Virtual Classroom
October 26 2021 - October 26 2021

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