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.
Author(s): Ashok Boddeda
Publisher: BPB Publications
Year: 2023
Language: English
Pages: 574
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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
