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Data Fabric and Data Mesh Approaches with AI: A Guide to Ai-Based Data Cataloging, Governance, Integration, Orchestration, and Consumption

Data Fabric and Data Mesh Approaches with AI: A Guide to Ai-Based Data Cataloging, Governance, Integration, Orchestration, and Consumption (Paperback)

Maryela Weihrauch, ,, Yan (Catherine) Wu (지은이)
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Apress
2023-04-01
  |  
91,230원

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Data Fabric and Data Mesh Approaches with AI: A Guide to Ai-Based Data Cataloging, Governance, Integration, Orchestration, and Consumption

책 정보

· 제목 : Data Fabric and Data Mesh Approaches with AI: A Guide to Ai-Based Data Cataloging, Governance, Integration, Orchestration, and Consumption (Paperback) 
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9781484292525
· 쪽수 : 427쪽

목차

Part I ? Data Fabric Foundation

This first part sets the scene for the book in terms of providing an introduction into the data fabric concept, how some of the terms, for instance data fabric and data mesh relate to each other, presenting some of the most relevant data fabric use case scenarios, and describing the key data fabric business benefits.

 

Chapter 1: Evolution of data architecture

By Maryela ? 5 pages

 

This chapter introduces the motivation for looking into data architectures. It shares an overview about data architecture evolution coming from traditional data warehouse to big data, data lake, their main characteristics, value, and challenges. It outlines industry requirements in a data-driven world that leads to the concept of a Data Fabric.

 

1)       Introduction

2)       Motivation

3)       Short history of data architectures, their value and challenges

4)       Industry requirements on a data architecture in a data-driven world

5)       Key Takeaways

6)       References

 

Chapter 2: Terminology ? Data Fabric and Data Mesh

By Eberhard ? 10 pages

 

This chapter explains the key terms that will be used throughout the book, particularly the terms data fabric and data mesh, and how these two terms relate to each other. We introduce the term ‘data-as-a-product’ and highlight the relationship of data fabric and data mesh concepts to DataOps, MLOps, AIDevOps ? among others.

 

1)       Introduction

2)       Data Fabric Concept

3)       AI-driven Data Fabric Concept

4)       Data Marketplace and Data-as-a-Product

5)       Data Mesh Concept

6)       The Relationship between Data Fabric and Data Mesh

7)       DataOps, MLOps, AIDevOps

8)       Key Takeaways

9)       References

 

Chapter 3: Data Fabric Use Case Scenarios

By Maryela ? 20 pages

 

We will walk through several use cases for implementing a Data Fabric that also would be good entry points. Data governance and privacy initiatives are ongoing in almost every organization, enabling access to enterprise data across platforms to the people who have a business need. Other use cases are driven by hybrid cloud data integration, the need for a comprehensive view on customers, vendors and other parties for better business outcome and development and integration of trustworthy AI into business processes.

 

1)       Introduction

2)       Establish an environment for automated and consistent governance

3)       Create a unified view of the data across the hybrid cloud

4)       Provide comprehensive view of customers and vendors

5)       Unlock trustworthy AI

6)       Key Takeaways

7)       References

 

Chapter 4: Data Fabric Business Benefits

By Maryela ? 10 pages

 

In this chapter we will dive into business needs and pain points that we hear in our conversations with enterprises. We will discuss business benefits of creating a Data Fabric from the perspective of the technical team as well as the business teams consuming the data.

 

1)       Introduction

2)       Business needs and pain points for data management and consumption

3)       Benefits of a Data Fabric for technical teams managing data

4)       Benefits of a Data Fabric for business teams consuming data

5)       Key Takeaways

6)       References

 

Part II ? Key Data Fabric Capabilities and Concepts

 

This second part presents key data fabric capabilities and focuses on AI and ML methods applied to those data fabric capabilities. It introduces the reader to the most relevant AI and ML concepts that are required to implement data fabric solutions. It furthermore discusses the AI usage for entity matching purposes, and how a data fabric implementation is leveraged for the entire AI lifecycle.

 

Chapter 5: Key Data Fabric Capabilities

By Eberhard ? 25 pages

 

This chapter introduces the key data fabric capabilities, such as self-service, AI and ML, trustworthy AI, intelligent information integration, active metadata ? among other topics. It also discusses knowledge graphs (semantic networks) as underpinning of a data fabric. A section on AI and ML to enable the ‘digital exhaust’ elaborates on pattern recognition and correlation discovery from the ‘digital exhaust’ to augment and operationalize this insight into the data fabric. Finally, active metadata management including rules and policy management and unified data governance are described.

 

1)       Introduction

2)       Self-service capabilities

3)       AI and ML

4)       Intelligent Information Integration

5)       Active Metadata

6)       Trustworthy AI

7)       Knowledge graphs and sematic networks

8)       Ontology and Taxonomy

9)       AI and ML to enable “Digital Exhaust” (overview with deep dive in chapter 7)

10)   Data Curation

11)   Unified Data Governance

12)   Rules and Policy Management

13)   Key Takeaways

14)   References

 

Chapter 6: Relevant AI and ML Concepts

By Catherine ? 12 pages

 

This chapter explains the key AI and ML concepts used for building data fabric capabilities. It starts with an introduction to AI, ML, and DL, their connections, and differences, and how these technologies are being used to accelerate enterprise digital transformation. Then it also introduces key techniques in the AI lifecycle. Starting from data, it explains the methods of understanding data and the techniques to transform data into a good shape suitable for model training. Then it discusses how to choose, train, and evaluate models, as well as how to deploy models to infuse AI/ML into data fabric solutions. Lastly, it covers National Languages Processing (NLP) and explains why it’s important to make use of unstructured data (especially text) in the context of data fabric for an Enterprise.

 

1)       Introduction to AI, ML, and DL

2)       ML and DL industry use cases

3)       Data Understanding and Preparation

4)       Model Selection, Training, and Evaluation

5)       Model Deployment

6)       National Language Processing (NLP)

7)       Key Takeaways

8)       References

 

Chapter 7: AI and ML for a Data Fabric

By Eberhard ? 20 pages

 

This chapter provides a deep dive into the exploitation of AI and ML for various topics and tasks, such as data discovery, profiling, and data access, to enable a ‘digital exhaust’, ML-based entity matching, automated data quality assessments, and semantic enrichment. This is an essential chapter, which highlights novel ideas to augment data fabric concepts with AI and ML.

 

1)       Introduction

2)       AI and ML for Data Discovery, Profiling and Access

3)       AI and ML to enable “Digital Exhaust”

4)       AI and ML for Entity Matching (overview with deep dive in chapter 8)

5)       AI and ML for automated Data Quality Assessment

6)       AI and ML for Semantic Enrichment

7)       Key Takeaways

8)       References

 

Chapter 8: AI for Entity Resolution

By Catherine ? 10 pages

 

This chapter explains what Entity Resolution, also known as Entity Matching is, why the problem has arisen and why it’s important to the business. Next, the reader will learn more about what are the traditional approaches to solving this problem and how artificial intelligence can reveal new possibilities for solving it, what will be the benefits and potential problems of using AI solutions, and how to choose a fit-for-purpose solution.

 

1)       Introduction

2)       What is Entity Matching and why does it matter - “Who are you in the digital world?”

3)       Traditional entity resolution approaches

4)       Use of AI to resolve entity challenges

5)       The benefits and the cost of AI-based solution

6)       Considerations for Entity Matching solutions

7)       Key Takeaways

8)       References

 

Chapter 9: Data Fabric for the AI Lifecycle

By Catherine ? 25 pages

 

This chapter explains data fabric capabilities during the entire AI lifecycle. First, it introduces the core ideas and concepts of AI engineering and how AI engineering relates to DataOps and MLOps. To help readers better understand the essence of the data fabric for the AI lifecycle, this chapter includes two case studies - the first one shows how data fabric can help in integrating data from various data sources in hybrid, multi-cloud enterprise environment, and the second case study introduces operationalizing AI and key benefits data fabric could bring to the production system such as security, explain-ability, governance, and scalability. It specifically highlights accelerating the implementation of MLOps with AutoAI. It further describes the best practices for operationalizing AI and common deployment patterns for AI engineering.

 

1.       The introduction to AI Engineering

2.       Key aspects of DataOps and MLOps

3.       Case study 1 - consolidating fragmented data in hybrid, multi-cloud environment

4.       Case study 2 - operationalizing AI

5.       Accelerate the implementation of MLOps with AutoAI

6.       Common deployment patterns for AI Engineering

7.       Key Takeaways

8.       References

 

Part III ? Deploying Data Fabric Solutions in Context

 

The third part introduces data fabric architecture patterns for different usage purposes, for instance intelligent data integration styles and data consumption patterns. It discusses the meaning of automated data fabric and intelligent cataloging and metadata management and describes the data fabric concept in the context of hybrid-cloud landscapes, an enterprise data architecture, and data governance initiatives.  

 

Chapter 10: Data Fabric Architecture Patterns

By Eberhard ? 15 pages

 

This chapter provides a high-level overview of the data fabric evolution, and elaborates on key data fabric architecture patterns, such as a data fabric architecture serving as the underpinning for a data mesh solution, intelligent information integration styles, and data consumption patterns.

 

1)       Introduction

2)       Data Fabric Architecture Evolution

3)       Data Fabric Architecture for a Data Mesh Solution

4)       Intelligent Information Integration Styles

5)       Data Consumption Patterns

6)       Key Takeaways

7)       References

 

Chapter 11: Role of Data Fabric in Hybrid-Cloud Landscape

By Maryela ? 15 pages

 

In this chapter, we will shortly introduce hybrid cloud, integrating IT on-prem and running in public cloud. This creates new challenges for accessing and integrating data across the organization. What is a data fabric in a hybrid cloud landscape?

 

1)       Introduction

2)       What is hybrid cloud?

3)       Challenges accessing and integrating data across the organization that runs IT in an hybrid cloud

4)       Data architecture evolution to implement Data Fabric in Hybrid Cloud

5)       Sample configurations

6)       Key take-aways

7)       References

 

Chapter 12: Data Fabric within an Enterprise Architecture

By Maryela ? 10 pages

 

Data architecture needs to be looked at in conjunction with the implemented application architecture in an enterprise. Many organizations are in the process to modernize their application and data landscape. Applications have different requirements in respect to data characteristics that may recommend one data architecture implementation over another, e.g., data access through virtualization or data replication and transformation.

 

1)       Introduction

2)       Application architecture drive data architecture decisions

3)       Data Fabric within an enterprise architecture

4)       Data Fabric Conceptual Model

5)       Key Takeaways

6)       References

 

Chapter 13: Intelligent Cataloging and Metadata Management

By Catherine ? 15 pages

 

This chapter introduces Metadata Management, followed by the key aspects of Intelligent Cataloging. It provides a deep dive into each key aspect and elaborates how data fabric capabilities realize automated data discovery, classify data assets and assign data assets with business terms, and establish enterprise knowledge graph by building the connections between data assets. It also highlights why data lineage and provenance are needed and how to implement them with data fabric.

 

1)       Introduction to Metadata Management

2)       Key aspects of Intelligent Cataloging ? data discovery, data profiling, and business term assignments

3)       Build an intelligent catalog by automating data discovery

4)       Enrich data assets with business definitions

5)       Understand the relationship between data assets

6)       Provide insights into data as it flows across the enterprise by lineage and provenance

7)       Key Takeaways

8)       References

 

Chapter 14: Automated Data Fabric

By Maryela ? 15 pages

 

The desire to create an enterprise-wide description of the data is not a new concept. It was considered a failure about 2 decades ago. This chapter describes the usage of intelligent automation to collect metadata information from different data sources and cataloging them, automatically check data quality and augment the data as well as automate data governance services as a foundation of a data fabric

 

1)       Introduction

2)       Intelligent automation of metadata collection

3)       Catalog and annotate meta data

4)       Automatically check quality and classify data

5)       Automated data governance services

6)       Key Takeaways

7)       References

 

Chapter 15: Data Governance and Data Fabric

By Catherine ? 20 pages

 

This chapter first explains why data governance and data privacy are critical to data-driven strategy for enterprises. Then it introduces the key aspects of data governance ? people, process, data regulations, data rules, data protection methods etc. It further explores how Data Fabric capabilities establish a data governance foundation for enterprise, make data trustworthy by automated quality analysis and protect data by automatic enforcement of data protection regulations.

 

1)       Introduction

2)       Why data governance important to enterprise

3)       Key aspects of data governance

4)       Establish a data governance foundation with Data Fabric

5)       Automated quality analysis with Data Fabric

6)       Automatic enforcement of data regulations with Data Fabric

7)       Key Takeaways

8)       References

 

 

Part IV ? Current Offerings and Future Aspects

 

The fourth part discusses a few sample vendor offerings and current data fabric research areas. This part finishes with a short summary and key takeaways.

 

Chapter 16: Sample Vendor Offerings

By Catherine ? 10 pages

 

The chapter will introduce how different vendors implement Data Fabric architecture with commercial software or SaaS. It further delves into each vendor’s offering and explains how it works and what are the strength respectively.

 

1)       Introduction

2)       IBM Cloud Pak for Data

3)       Amazon Web Services

4)       Microsoft Azure

5)       Denodo

6)       K2View

7)       Key Takeaways

8)       References

 

Chapter 17: Data Fabric Research Areas

By Eberhard ? 10 pages

 

This Chapter discusses data fabric challenges as they are addressed by current data fabric research initiatives, such as hyper-automation, knowledge-based consumption. It also describes the confluence of AI and the data fabric and outlines the road towards an AI-driven information fabric.

 

1)       Introduction

2)       Data Fabric Challenges

3)       The Confluence of AI and Data Fabric

4)       Hyper-Automation

5)       Knowledge-based Consumption

6)       Towards an AI-driven Information Fabric

7)       Key Takeaways

8)       References

 

Chapter 18: In Summary and Onwards

By Maryela ? 5 pages

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