Course details

Data Architecture - Deep Dive: Microservices & Serverless Computing

Data Architecture - Deep Dive: Microservices & Serverless Computing


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Explore numerous types of data architecture that are effective data wrangling tools when working with big data in this 9-video Skillsoft Aspire course. Learn the strategies, design, and constraints involved in implementing data architecture. You will learn the concepts of data partitioning, CAP theorem (consistency, availability, and partition tolerance), and process implementation using serverless and Lambda data architecture. This course examines Saga, newly introduced in data management pattern catalog of microservices; API (application programming interface) composition; CQRS (Command Query Responsibility Segregation); event sourcing; and application event. This course explores the differences in traditional data architecture and serverless architecture which allows you to use client-side logic and third-party services. You will learn how to use AWS (Amazon Web Services) Lambda to implement a serverless architecture. This course then explores batch processing architecture, which processes data files by using long running batch jobs to filter actual content, real-time architecture, and machine learning at scale architecture built to serve machine learning algorithms. Finally, you will explore how to build a successful data POC (proof of concept).



Expected Duration (hours)
0.4

Lesson Objectives

Data Architecture - Deep Dive: Microservices & Serverless Computing

  • Course Overview
  • describe data pattern implementation in microservices
  • describe the beneficial features of serverless and Lambda architectures
  • demonstrate how to implement Lambda architecture in AWS
  • manage resources with the implementation of clusters
  • describe data architecture implementations and their advantages
  • specify the steps involved in discovering and deriving value from data in existing datasets
  • classify the different types of data risks that need to be managed when implementing data modeling and design
  • specify the steps involved in building a successful data POC
  • recall the beneficial features of Lambda and serverless architecture and specify the essential processes of discovering data
  • Course Number:
    it_dsfddadj_02_enus

    Expertise Level
    Intermediate