Deciphering Data Architectures (Record no. 359821)
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| 000 -LEADER | |
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| fixed length control field | 09759 a2200169 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20241018162706.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 241018b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789355425928 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | English |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Author | Serra J. |
| 245 ## - TITLE STATEMENT | |
| Title | Deciphering Data Architectures |
| Remainder of title | Choosing Between A Modern Data Warehouse, Data Fabric, Data Lakehouse And Data Mesh |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Name of publisher, distributor, etc. | SPD |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 250 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Table of Contents<br/>Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii<br/>Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xix<br/>Part I. Foundation<br/>1. Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3<br/>What Is Big Data, and How Can It Help You? 4<br/>Data Maturity 7<br/>Stage 1: Reactive 8<br/>Stage 2: Informative 8<br/>Stage 3: Predictive 9<br/>Stage 4: Transformative 9<br/>Self-Service Business Intelligence 9<br/>Summary 10<br/>2. Types of Data Architectures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13<br/>Evolution of Data Architectures 14<br/>Relational Data Warehouse 16<br/>Data Lake 18<br/>Modern Data Warehouse 20<br/>Data Fabric 21<br/>Data Lakehouse 21<br/>Data Mesh 22<br/>Summary 23<br/>ix<br/>3. The Architecture Design Session. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25<br/>What Is an ADS? 25<br/>Why Hold an ADS? 26<br/>Before the ADS 27<br/>Preparing 27<br/>Inviting Participants 29<br/>Conducting the ADS 31<br/>Introductions 31<br/>Discovery 31<br/>Whiteboarding 36<br/>After the ADS 37<br/>Tips for Conducting an ADS 38<br/>Summary 40<br/>Part II. Common Data Architecture Concepts<br/>4. The Relational Data Warehouse. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43<br/>What Is a Relational Data Warehouse? 43<br/>What a Data Warehouse Is Not 46<br/>The Top-Down Approach 47<br/>Why Use a Relational Data Warehouse? 49<br/>Drawbacks to Using a Relational Data Warehouse 52<br/>Populating a Data Warehouse 53<br/>How Often to Extract the Data 53<br/>Extraction Methods 54<br/>How to Determine What Data Has Changed Since the Last Extraction 54<br/>The Death of the Relational Data Warehouse Has Been Greatly Exaggerated 56<br/>Summary 57<br/>5. Data Lake. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59<br/>What Is a Data Lake? 60<br/>Why Use a Data Lake? 60<br/>Bottom-Up Approach 62<br/>Best Practices for Data Lake Design 63<br/>Multiple Data Lakes 69<br/>Advantages 69<br/>Disadvantages 72<br/>Summary 72<br/>x | Table of Contents<br/>6. Data Storage Solutions and Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75<br/>Data Storage Solutions 76<br/>Data Marts 76<br/>Operational Data Stores 77<br/>Data Hubs 79<br/>Data Processes 81<br/>Master Data Management 81<br/>Data Virtualization and Data Federation 82<br/>Data Catalogs 87<br/>Data Marketplaces 87<br/>Summary 89<br/>7. Approaches to Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91<br/>Online Transaction Processing Versus Online Analytical Processing 92<br/>Operational and Analytical Data 94<br/>Symmetric Multiprocessing and Massively Parallel Processing 94<br/>Lambda Architecture 96<br/>Kappa Architecture 98<br/>Polyglot Persistence and Polyglot Data Stores 100<br/>Summary 101<br/>8. Approaches to Data Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103<br/>Relational Modeling 103<br/>Keys 103<br/>Entity–Relationship Diagrams 104<br/>Normalization Rules and Forms 104<br/>Tracking Changes 106<br/>Dimensional Modeling 107<br/>Facts, Dimensions, and Keys 107<br/>Tracking Changes 108<br/>Denormalization 109<br/>Common Data Model 111<br/>Data Vault 111<br/>The Kimball and Inmon Data Warehousing Methodologies 113<br/>Inmon’s Top-Down Methodology 114<br/>Kimball’s Bottom-Up Methodology 115<br/>Choosing a Methodology 117<br/>Hybrid Models 118<br/>Methodology Myths 120<br/>Summary 123<br/>Table of Contents | xi<br/>9. Approaches to Data Ingestion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125<br/>ETL Versus ELT 125<br/>Reverse ETL 127<br/>Batch Processing Versus Real-Time Processing 129<br/>Batch Processing Pros and Cons 130<br/>Real-Time Processing Pros and Cons 130<br/>Data Governance 131<br/>Summary 132<br/>Part III. Data Architectures<br/>10. The Modern Data Warehouse. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135<br/>The MDW Architecture 135<br/>Pros and Cons of the MDW Architecture 140<br/>Combining the RDW and Data Lake 142<br/>Data Lake 142<br/>Relational Data Warehouse 142<br/>Stepping Stones to the MDW 143<br/>EDW Augmentation 143<br/>Temporary Data Lake Plus EDW 145<br/>All-in-One 146<br/>Case Study: Wilson & Gunkerk’s Strategic Shift to an MDW 147<br/>Challenge 147<br/>Solution 147<br/>Outcome 148<br/>Summary 148<br/>11. Data Fabric. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151<br/>The Data Fabric Architecture 152<br/>Data Access Policies 154<br/>Metadata Catalog 154<br/>Master Data Management 155<br/>Data Virtualization 155<br/>Real-Time Processing 155<br/>APIs 155<br/>Services 156<br/>Products 156<br/>Why Transition from an MDW to a Data Fabric Architecture? 156<br/>Potential Drawbacks 157<br/>Summary 157<br/>xii | Table of Contents<br/>12. Data Lakehouse. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159<br/>Delta Lake Features 160<br/>Performance Improvements 162<br/>The Data Lakehouse Architecture 163<br/>What If You Skip the Relational Data Warehouse? 165<br/>Relational Serving Layer 167<br/>Summary 167<br/>13. Data Mesh Foundation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169<br/>A Decentralized Data Architecture 170<br/>Data Mesh Hype 171<br/>Dehghani’s Four Principles of Data Mesh 172<br/>Principle #1: Domain Ownership 172<br/>Principle #2: Data as a Product 173<br/>Principle #3: Self-Serve Data Infrastructure as a Platform 175<br/>Principle #4: Federated Computational Governance 176<br/>The “Pure” Data Mesh 177<br/>Data Domains 178<br/>Data Mesh Logical Architecture 179<br/>Different Topologies 181<br/>Data Mesh Versus Data Fabric 182<br/>Use Cases 183<br/>Summary 185<br/>14. Should You Adopt Data Mesh? Myths, Concerns, and the Future. . . . . . . . . . . . . . . . . 187<br/>Myths 187<br/>Myth: Using Data Mesh Is a Silver Bullet That<br/>Solves All Data Challenges Quickly 187<br/>Myth: A Data Mesh Will Replace Your Data Lake and Data Warehouse 188<br/>Myth: Data Warehouse Projects Are All Failing,<br/>and a Data Mesh Will Solve That Problem 188<br/>Myth: Building a Data Mesh Means Decentralizing Absolutely Everything 188<br/>Myth: You Can Use Data Virtualization to Create a Data Mesh 189<br/>Concerns 190<br/>Philosophical and Conceptual Matters 190<br/>Combining Data in a Decentralized Environment 191<br/>Other Issues of Decentralization 192<br/>Complexity 193<br/>Duplication 193<br/>Feasibility 194<br/>People 196<br/>Domain-Level Barriers 197<br/>Table of Contents | xiii<br/>Organizational Assessment: Should You Adopt a Data Mesh? 198<br/>Recommendations for Implementing a Successful Data Mesh 199<br/>The Future of Data Mesh 201<br/>Zooming Out: Understanding Data Architectures and Their Applications 202<br/>Summary 203<br/>Part IV. People, Processes, and Technology<br/>15. People and Processes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207<br/>Team Organization: Roles and Responsibilities 208<br/>Roles for MDW, Data Fabric, or Data Lakehouse 208<br/>Roles for Data Mesh 210<br/>Why Projects Fail: Pitfalls and Prevention 213<br/>Pitfall: Allowing Executives to Think That BI Is “Easy” 213<br/>Pitfall: Using the Wrong Technologies 213<br/>Pitfall: Gathering Too Many Business Requirements 213<br/>Pitfall: Gathering Too Few Business Requirements 214<br/>Pitfall: Presenting Reports Without Validating Their Contents First 214<br/>Pitfall: Hiring an Inexperienced Consulting Company 214<br/>Pitfall: Hiring a Consulting Company That Outsources<br/>Development to Offshore Workers 215<br/>Pitfall: Passing Project Ownership Off to Consultants 215<br/>Pitfall: Neglecting the Need to Transfer Knowledge<br/>Back into the Organization 215<br/>Pitfall: Slashing the Budget Midway Through the Project 215<br/>Pitfall: Starting with an End Date and Working Backward 216<br/>Pitfall: Structuring the Data Warehouse to Reflect the<br/>Source Data Rather Than the Business’s Needs 216<br/>Pitfall: Presenting End Users with a Solution with Slow Response Times or<br/>Other Performance Issues 216<br/>Pitfall: Overdesigning (or Underdesigning) Your Data Architecture 217<br/>Pitfall: Poor Communication Between IT and the Business Domains 217<br/>Tips for Success 217<br/>Don’t Skimp on Your Investment 217<br/>Involve Users, Show Them Results, and Get Them Excited 218<br/>Add Value to New Reports and Dashboards 219<br/>Ask End Users to Build a Prototype 219<br/>Find a Project Champion/Sponsor 219<br/>Make a Project Plan That Aims for 80% Efficiency 220<br/>Summary 220<br/>xiv | Table of Contents<br/>16. Technologies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223<br/>Choosing a Platform 223<br/>Open Source Solutions 223<br/>On-Premises Solutions 226<br/>Cloud Provider Solutions 227<br/>Cloud Service Models 230<br/>Major Cloud Providers 232<br/>Multi-Cloud Solutions 232<br/>Software Frameworks 235<br/>Hadoop 235<br/>Databricks 238<br/>Snowflake 240<br/>Summary 241<br/>Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243<br/>Table of Contents | xv |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Cummins College of Engineering for Women Pune | Cummins College of Engineering for Women Pune | 14/10/2024 | 115 | 1050.00 | 005.74 SER | CCEP-BK-67508 | 14/10/2024 | 1400.00 | 14/10/2024 | Books |
