• Online, Self-Paced
Course 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.

Learning 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

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.