Data Architecture - Deep Dive: Design & Implementation

Data Architecture - Deep Dive: Design & Implementation

• Course Overview
• describe data complexity management strategies
• recognize data modeling techniques and describe data modeling processes
• list prominent distributed data models and their associative implementation benefits
• describe data partitioning methods and data partitioning implementation criteria
• install MongoDB and implement data partitioning using MongoDB
• identify important components of a hybrid data architecture
• demonstrate how to implement directed acyclic graphs using Elasticsearch
• describe CAP theorems and their implementation approaches
• compare the differences between batch and streaming data
• recognize the read and write optimizations in MongoDB
• implement serverless architecture with Lambda and data partitioning using MongoDB

This 11-video Skillsoft Aspire course explores the numerous types of data architecture that can be used when working with big data; how to implement strategies by using NoSQL (not only structured query language); CAP theorem (consistency, availability, and partition tolerance); and partitioning to improve performance. Learners examine the core activities essential for data architectures: data security, privacy, integrity, quality, regulatory compliances, and governance. You will learn different methods of partitioning, and the criteria for implementing data partitioning. Next, you will install and explore MongoDB, a cross-platform document-oriented database system, and learn to read and write optimizations in MongoDB. You will learn to identify various important components of hybrid data architecture, and adapting it to your data needs. You will learn how to implement DAG (Directed Acyclic Graph) by using the Elasticsearch search engine. You evaluate your needs to determine whether to implement batch processing or stream processing. This course also covers process implementation by using serverless and Lambda architecture. Finally, you will examine types of data risk when implementing data modeling and design.

Data Architecture - Deep Dive: Microservices & Serverless Computing

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

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).