Data Architecture Deep Dive - Design & Implementation

Data Science | Intermediate

12 Videos | 40m 8s
Includes Assessment
Earns a Badge

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.

WHAT YOU WILL LEARN

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

IN THIS COURSE

1.

Course Overview

1m 46s

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2.

Data Complexity Management Strategies

4m 23s
3.

Data Modeling Process

2m 58s

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4.

Distributed Data Management

2m 40s

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5.

Partitioning Methods and Criteria

2m 23s

FREE ACCESS
6.

MongoDB Partitioning

5m 16s

FREE ACCESS
7.

Hybrid Data Architectures

2m 57s

FREE ACCESS
8.

Implement Directed Acyclic Graph

2m 52s

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9.

CAP Theorem

3m 7s

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10.

Batch vs. Streaming

2m 10s

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11.

Read and Write Concerns

2m 22s

FREE ACCESS
12.

Exercise: Implement Serverless Architecture

2m 13s

FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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