Cloud Data Architectures Demystified: Gain the expertise to build Cloud data solutions as per the organization's needs

✍ Scribed by Ashok Boddeda

• Publisher: BPB Publications
• Year: 2023
• Tongue: English
• Leaves: 574
• Category: Library

✦ Synopsis

Learn using Cloud data technologies for improving data analytics and decision-making capabilities for your organization.

Description
Cloud data architectures are a valuable tool for organizations that want to use data to make better decisions. By understanding the different components of Cloud data architectures and the benefits they offer, organizations can select the right architecture for their needs.

This book is a holistic guide for using Cloud data technologies to ingest, transform, and analyze data. It covers the entire data lifecycle, from collecting data to transforming it into actionable insights. The readers will get a comprehensive overview of Cloud data technologies and AI/ML algorithms. The readers will learn how to use these technologies and algorithms to improve decision-making, optimize operations, and identify new opportunities.

By the end of the book, you will have a comprehensive understanding of loud data architectures and the confidence to implement effective solutions that drive business success.

What you will learn:
- Learn the fundamental principles of data architecture.
- Understand the working of different cloud ecosystems such as AWS, Azure & GCP.
- Explore different Snowflake data services.
- Learn how to implement data governance policies and procedures.

Chapter 1: Data Architectures and Patterns
This opening chapter lays the foundation by unraveling the fundamental concepts that underpin data architectures and patterns. We delve into the crucial role these concepts play in organizing and optimizing data for efficient processing and analysis, setting the stage for the following chapters.

Chapter 2: Enterprise Data Architectures
The chapter delves into the heart of data management within organizations. From designing robust data pipelines to constructing scalable storage solutions, we explore how enterprises can establish architectures that meet their unique needs while ensuring data availability, integrity, and security.

Chapter 3: Cloud Fundamentals
As Cloud computing takes center stage in modern IT landscapes, the chapter breaks down the core principles of cloud computing. We unravel the basic tenets that enable the Cloud's transformative capabilities, empowering readers to comprehend the underlying mechanics driving cloud-based data solutions.

Chapter 4: Azure Data Eco-system
In this chapter, we delve into Microsoft's Azure eco-system, examining its suite of data services and offerings. From databases to analytics tools, we navigate the vast Azure landscape, offering insights into how each component contributes to shaping robust Cloud data architectures.

Chapter 5: AWS Data Services
This chapter embarks on a similar exploration, this time focusing on Amazon Web Services (AWS) data services. We traverse AWS's breadth of offerings, illuminating the pathways to building efficient data storage, processing, and analysis strategies within the AWS environment.

Chapter 6: Google Data Services
Google's presence in the cloud is undeniable, and this chapter delves into its data services. From BigQuery to Cloud Storage, we unravel Google's solutions, demonstrating how they can be harnessed to construct agile, data-driven architectures.

Chapter 7: Snowflake Data Eco-system
This chapter highlights Snowflake, an increasingly popular cloud-based data warehousing solution. We explore Snowflake's capabilities and role in reshaping traditional data warehousing models, paving the way for more flexible and efficient architectures.

Chapter 8: Data Governance
Data without governance risks becoming chaotic and unreliable. We will delve into the critical domain of data governance, elucidating the strategies, policies, and practices necessary to ensure data quality, compliance, and security.

Chapter 9: Data Intelligence: AI-ML Modeling and Services
The final chapter ventures into data intelligence, where Artificial Intelligence and Machine Learning (AI/ML) take center stage. We explore how AI/ML can be integrated into data architectures to unlock predictive and prescriptive insights, revolutionizing decision-making processes.

Who this book is for:
This book is for executives, IT professionals, and data enthusiasts who want to learn more about Cloud data architectures. It does not require any prior experience, but a basic understanding of data concepts and technology landscapes will be helpful.

✦ Table of Contents

1. Data Architectures and Patterns

Introduction

Structure

Objectives

Data architecture

Benefits of well-designed data architectures

Data architecture components

Data capture

Data storage

Data transformation

Data analytics

Data intelligence

Types of data architectures

Centralized data architectures

Decentralized data architectures

Distributed and modern data architectures

Data Lakehouse

Data Hub

Data Mesh

Data fabric

Data architectures comparison

Designing effective data architecture

Conclusion

Key facts

Multiple choice questions

Answers

1. Enterprise Data Architectures

Introduction

Structure

Objectives

Understanding data

Sources of data

Types of data

Big Data overview

The 4 Vs of Big Data

Volume

Velocity

Variety

Veracity

Data lifecycle

Data ingest

Data store

Data preparation

Data serve

Data reporting

Analogy aids in understanding

Baking a cake

Data processing architectures

Lambda architecture

Kappa architecture

Big Data complete architecture

Enterprise data management services

Enterprise data architecture

Conclusion

Key facts

Multiple choice questions

Answers

1. Cloud Fundamentals

Introduction

Structure

Objectives

On-premises data center

Limitations of the on-premises data center

Cloud computing

Cloud computing service models

Infrastructure as service (IaaS)

Platform as a service (PaaS)

Software as a service (SaaS)

Types of Cloud deployment models

Public Cloud

Private Cloud

Hybrid Cloud

Benefits of the Cloud

Azure fundamentals

What is Azure?

Azure regions and availability zones

Azure data redundancy

Azure Cloud services

Azure Virtual Machines

Azure storage

Azure Virtual Networks

Network security group and access control list

Azure Identity – active directory

Basic Cloud IaaS architecture

Azure application and data services

Azure data and analytical services

Database services

Data analytical services

Azure Marketplace

Azure management tools

Azure pricing models

Pay-as-you-go

Enterprise Agreement

Cloud Solution Provider

Azure support plans

Conclusion

Key facts

Multiple choice questions

Answers

1. Azure Data Eco-system

Introduction

Structure

Objectives

Data classification

Key features of Azure Storage

Scalability

Availability

Security

Accessibility

Access tiers

Storage options in Azure

Unstructured storage

Azure Blobs

Azure Managed disks

Azure File storage

Azure Datalake Gen1/Gen2

Difference between Azure blob storage and Azure Datalake

Enterprise use cases of Datalake and Blob storage

Structured storage

Azure IaaS relational storage

Azure PaaS relational storage

Semi-structured storage

Azure Queues

EventHub

Azure Service Bus

ETL overview

Azure Data Factory

Fundamental tasks of ADF

Data Ingest

Control flow

Data flow

Scheduling

Azure Data analytic solutions

Azure Synapse Analytics

Azure HDInsight

Azure Databricks

Azure Big Data solutions

Azure Big Data architecture

Conclusion

Key facts

Multiple choice questions

Answers

1. AWS Data Services

Introduction

Structure

Objectives

Key characteristics of AWS storage

AWS storage options

Unstructured storage

Object storage

File storage

Block storage

AWS Simple Storage Service (S3)

Key features of S3

Semi-structured storage

AWS DocumentDB

Key features of DocumentDB

AWS DynamoDB

AWS Kinesis

Amazon Kinesis Data Streams

Amazon Kinesis Video Streams

Amazon Kinesis Firehose

Amazon Kinesis Data Analytics

Amazon Simple Queue Service (Amazon SQS)

Structured storage

Amazon RDS

Amazon Redshift

Amazon Redshift performance

AWS Aurora

AWS Elastic Cache

Business use-cases for each tool

AWS Datalake storage

AWS Lakehouse

AWS data orchestration

AWS Glue

AWS Data Pipeline

AWS Analytics Solutions

AWS AIML services

Conclusion

Key facts

Multiple choice questions

Answers

References

1. Google Data Services

Introduction

Structure

Objectives

Google Cloud Platform

Google Storage

Google storage options

Unstructured storage services in Google

Cloud object store

Google Cloud Persistent Disks (Block storage)

Google Cloud Filestore (Network File Storage)

Storage classes

Semi-structured storage services

Google Firestore

Google Cloud Pub/Sub

Structured storage services

Cloud SQL

Google Cloud Spanner

Google BigTable

Cloud Datastore

Google Data Lake solution

Google Data orchestration or Pipeline solution

Google Dataflow

Google Datafusion

Google cloud workflows

Workflow structure

Integration

Scalability and reliability

Use cases

Google Cloud Composure

Google BigQuery

Key usage of BigQuery

BigQuery architecture

Conclusion

Key facts

Multiple choice questions

Answers

References

1. Snowflake Data Eco-system

Introduction

Structure

Objectives

Snowflake database

Key features of Snowflake

Benefits of the Snowflake database

Snowflake data architecture

Data loading and unloading

Snowflake data loading

Snowflake data unloading

Querying data in the Snowflake database

Query language

Query execution

Query optimization

Resultset management

Query history and monitoring

Integration with Business Intelligence and Analytics tools

Snowflake virtual Warehouses and data sharing

Snowflake security features

Snowflake integrations

Conclusion

Key facts

Multiple choice questions

Answers

References

1. Data Governance

Introduction

Structure

Objectives

Data governance

Key pillars of data governance

Data quality

Data lineage

Data privacy and security

Data governance framework

Data catalog

Types of data catalog

Benefits of data catalogs

Data catalog management

Data stewardship

Market players in data governance

Comparison table: Alation, Collibra and Informatica

Data governance tools by Cloud providers

Azure data governance tools

AWS data governance tools

Google data governance tool

Snowflake data governance

Role-Based Access Control

Data sharing and data sharing controls

Data masking and secure views

Time travel and data retention policies

Multi-factor authentication

Auditing and access history

Data classification and tagging

Usage monitoring and query profiling

Resource governance

Compliance certifications

Conclusion

Key facts

Multiple choice questions

Answers

1. Data Intelligence: AI-ML Modeling and Services

Introduction

Structure

Objectives

AI-ML transformation

The business impact of AI

Key aspects of AI

AI for problem solving: Process automation and efficiency

AI for knowledge representation: enhancing business intelligence

AI and machine learning models and their business applications

Supervised learning for predictive analytics

Unsupervised learning for market segmentation and customer insights

Semi-supervised learning for leveraging partially labeled data

Reinforcement learning for dynamic decision making

Neural networks and deep learning

Understanding deep learning

Harnessing deep learning for advanced business applications

AI-ML services

Accelerating AI excellence with MLOps

Data ingestion

Data validation

Feature extraction

Model training

Model evaluation

Model deployment

Monitoring and maintenance

Feedback loop and iterative improvement

Generative AI

ChatGPT

ChatGPT Enterprise usecases

Ethics, bias, and fairness in AI and ML

Understanding bias in AI and ML

AI, ML, and the question of fairness

Broader ethical implications

Responsible AI

Conclusion

Key facts

Multiple choice questions

Answers

Index