Building a Serverless Data Lake on AWS (BSDL)

Tijdsduur
Startdatum en plaats
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Startdata en plaatsen

Utrecht
20 feb. 2020

Beschrijving

Course Content

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.

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
  • F…

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Course Content

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.

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

Who Should Attend

This course is intended for:

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

Gedetailleerde cursusinhoud

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

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