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Data Analytics in the AWS Cloud
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Building a Data Platform for BI and Predictive Analytics on AWS
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Artikel-Nr:
9781119909248
Veröffentl:
2023
Erscheinungsdatum:
18.05.2023
Seiten:
416
Autor:
Joe Minichino
Gewicht:
680 g
Format:
231x185x23 mm
Sprache:
Englisch
Beschreibung:
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.
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.
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.
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
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