Joe Minichino

Data Analytics in the AWS Cloud (eBook, ePUB)

Building a Data Platform for BI and Predictive Analytics on AWS

• Format: ePub

Produktbeschreibung

A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you'll explore every relevant aspect of data analytics--from data engineering to analysis, business intelligence, DevOps, and MLOps--as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You'll also find: * Real-world use cases of AWS architectures that demystify the applications of data analytics * Accessible introductions to data acquisition, importation, storage, visualization, and reporting * Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 400
• Erscheinungstermin: 6. April 2023
• Englisch
• ISBN-13: 9781119909255
• Artikelnr.: 67783544

Autorenporträt

GIONATA "JOE" MINICHINO is Principal Software Engineer and Data Architect on the Data & Analytics Team at Teamwork. He specializes in cloud computing, machine/deep learning, and artificial intelligence and designs end-to-end Amazon Web Services pipelines that move large quantities of diverse data for analysis and visualization.

Inhaltsangabe

Introduction xxiii

Chapter 1 AWS Data Lakes and Analytics Technology Overview 1

Why AWS? 1

What Does a Data Lake Look Like in AWS? 2

Analytics on AWS 3

Skills Required to Build and Maintain an AWS Analytics Pipeline 3

Chapter 2 The Path to Analytics: Setting Up a Data and Analytics Team 5

The Data Vision 6

Support 6

DA Team Roles 7

Early Stage Roles 7

Team Lead 8

Data Architect 8

Data Engineer 8

Data Analyst 9

Maturity Stage Roles 9

Data Scientist 9

Cloud Engineer 10

Business Intelligence (BI) Developer 10

Machine Learning Engineer 10

Business Analyst 11

Niche Roles 11

Analytics Flow at a Process Level 12

Workflow Methodology 12

The DA Team Mantra: "Automate Everything" 14

Analytics Models in the Wild: Centralized, Distributed, Center of Excellence 15

Centralized 15

Distributed 16

Center of Excellence 16

Summary 17

Chapter 3 Working on AWS 19

Accessing AWS 20

Everything Is a Resource 21

S3: An Important Exception 21

IAM: Policies, Roles, and Users 22

Policies 22

Identity- Based Policies 24

Resource- Based Policies 25

Roles 25

Users and User Groups 25

Summarizing IAM 26

Working with the Web Console 26

The AWS Command- Line Interface 29

Installing AWS cli 29

Linux Installation 30

macOS Installation 30

Windows 31

Configuring AWS cli 31

A Note on Region 33

Setting Individual Parameters 33

Using Profiles and Configuration Files 33

Final Notes on Configuration 36

Using the AWS cli 36

Using Skeletons and File Inputs 39

Cleaning Up! 43

Infrastructure- as- Code: CloudFormation and Terraform 44

CloudFormation 44

CloudFormation Stacks 46

CloudFormation Template Anatomy 47

CloudFormation Changesets 52

Getting Stack Information 55

Cleaning Up Again 57

CloudFormation Conclusions 58

Terraform 58

Coding Style 58

Modularity 59

Limitations 59

Terraform vs. CloudFormation 60

Infrastructure- as- Code: CDK, Pulumi, Cloudcraft, and Other Solutions 60

AWS CDK 60

Pulumi 62

Cloudcraft 62

Infrastructure Management Conclusions 63

Chapter 4 Serverless Computing and Data Engineering 65

Serverless vs. Fully Managed 65

AWS Serverless Technologies 66

AWS Lambda 67

Pricing Model 67

Laser Focus on Code 68

The Lambda Paradigm Shift 69

Virtually Infinite Scalability 70

Geographical Distribution 70

A Lambda Hello World 71

Lambda Configuration 74

Runtime 74

Container- Based Lambdas 75

Architectures 75

Memory 75

Networking 76

Execution Role 76

Environment Variables 76

AWS EventBridge 77

AWS Fargate 77

AWS DynamoDB 77

AWS SNS 77

Amazon SQS 78

AWS CloudWatch 78

Amazon QuickSight 78

AWS Step Functions 78

Amazon API Gateway 79

Amazon Cognito 79

AWS Serverless Application Model (SAM) 79

Ephemeral Infrastructure 80

AWS SAM Installation 80

Config