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Data Silos, Lakes, & Streams: Introduction

Data Silos, Lakes, & Streams: Introduction


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Traditional data warehousing is transitioning to be more cloud-based and this can be a key area that must be mastered for data science. In this course you will examine the organizational implications of data silos and explore how data lakes can help make data secure, discoverable, and queryable. Discover how data lakes can work with batch and streaming data.



Expected Duration (hours)
1.3

Lesson Objectives

Data Silos, Lakes, & Streams: Introduction

  • recall the characteristics and drawbacks of data silos
  • specify what a data lake enables
  • recognize the advantages of using data lakes to store data
  • describe the architecture of a data lake and identify challenges in its design
  • recall the characteristics of a data warehouse
  • specify the differences between data warehouses and data lakes
  • distinguish between batch and streaming data and recognize the Stream-First Architecture
  • describe how data can be moved from on-premise to the AWS cloud platform
  • recognize the technologies used to build data lakes on AWS
  • describe various use cases and architectures of working with data lakes on AWS
  • recall characteristics of data silos, data lakes, and data streams
  • Course Number:
    it_dsdslsdj_01_enus

    Expertise Level
    Intermediate