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AWS DISCOVERY DAY: FUNDAMENTALS OF A MODERN DATA STRATEGY ON AWS

  • Kursuskode GKAWS-MDS
  • Varighed 1 Dag

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Kurset er tilgængeligt i følgende formater:

  • Firma kursus

    Et lukket firma kursus

  • Åbent kursus

    Traditionel klasserumsundervisning

  • Åbent kursus (Virtuelt)

    Live klasserumsundervisning du tilgår virtuelt

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Learn how to modernize, unify, and innovate your way to a modern data strategy with AWS.

Organizations need a data strategy to succeed, but there’s no one-size fits all approach. Every organization will have unique objectives and obstacles. Join this event to learn how to modernize, unify, and innovate your way to a modern data strategy with AWS.

- Level: Fundamental
- Duration: 1.5 hours

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

Kursusdato

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Målgruppe

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This event is intended for:

- Data architects, data scientists, and data analysts new to the AWS Cloud
- Solutions architects
- Database administrators

Kursets formål

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During this event, you will learn:

  • How AWS services help you modernize, unify, and innovate your data infrastructure
  • How to use AWS services to provide secure and well-governed access to data
  • How to innovate with AI/ML by harnessing your data with built-in ML

Kursusindhold

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Section 1: Introduction

  • Business value of a modern data strategy
  • Challenges
  • Use cases
  • Traditional vs. cloud
  • Modern data strategy on AWS

Section 2: Modernize

  • Relational databases
  • Purpose-built databases

Section 3: Unify

  • Data lakes with Amazon S3
  • Lake house approach on AWS
  • Purpose-built data services
  • Breaking down data silos
  • Unified governance

Section 4: Innovate

  • Machine learning (ML) services
  • Artificial intelligence (AI) services
  • Data stores, data lakes, and BI tools with built-in ML

Section 5: Next steps

  • Resources to continue learning