کتاب رمزگشایی معماری‌های داده

کلان‌داده

What Is Big Data, and How Can It Help You? • Data Maturity • Stage 1: Reactive • Stage 2: Informative • Stage 3: Predictive • Stage 4: Transformative • Self-Service Business Intelligence • Summary

انواع معماری‌های داده

Evolution of Data Architectures • Relational Data Warehouse • Data Lake • Modern Data Warehouse • Data Fabric • Data Lakehouse • Data Mesh • Summary

جلسه طراحی معماری

What Is an ADS? • Why Hold an ADS? • Before the ADS • Preparing • Inviting Participants • Conducting the ADS • Introductions • Discovery • Whiteboarding • After the ADS • Tips for Conducting an ADS • Summary

دیتا ورهوس رابطه‌ای

What Is a Relational Data Warehouse? • What a Data Warehouse Is Not • The Top-Down Approach • Why Use a Relational Data Warehouse? • Drawbacks to Using a Relational Data Warehouse • Populating a Data Warehouse • How Often to Extract the Data • Extraction Methods • How to Determine What Data Has Changed Since the Last Extraction • The Death of the Relational Data Warehouse Has Been Greatly Exaggerated • Summary

دیتا لیک

What Is a Data Lake? • Why Use a Data Lake? • Bottom-Up Approach • Best Practices for Data Lake Design • Multiple Data Lakes • Advantages • Disadvantages • Summary

راه‌حل‌ها و فرآیندهای ذخیره‌سازی داده

Data Storage Solutions • Data Marts • Operational Data Stores • Data Hubs • Data Processes • Master Data Management • Data Virtualization and Data Federation • Data Catalogs • Data Marketplaces • Summary

رویکردهای طراحی

Online Transaction Processing Versus Online Analytical Processing • Operational and Analytical Data • Symmetric Multiprocessing and Massively Parallel Processing • Lambda Architecture • Kappa Architecture • Polyglot Persistence and Polyglot Data Stores • Summary

رویکردهای مدل‌سازی داده

Relational Modeling • Keys • Entity–Relationship Diagrams • Normalization Rules and Forms • Tracking Changes • Dimensional Modeling • Facts, Dimensions, and Keys • Tracking Changes • Denormalization • Common Data Model • Data Vault • The Kimball and Inmon Data Warehousing Methodologies • Inmon’s Top-Down Methodology • Kimball’s Bottom-Up Methodology • Choosing a Methodology • Hybrid Models • Methodology Myths • Summary

رویکردهای ورود داده

ETL Versus ELT • Reverse ETL • Batch Processing Versus Real-Time Processing • Batch Processing Pros and Cons • Real-Time Processing Pros and Cons • Data Governance • Summary

دیتا ورهوس مدرن

The MDW Architecture • Pros and Cons of the MDW Architecture • Combining the RDW and Data Lake • Data Lake • Relational Data Warehouse • Stepping Stones to the MDW • EDW Augmentation • Temporary Data Lake Plus EDW • All-in-One • Case Study: Wilson & Gunkerk’s Strategic Shift to an MDW • Challenge • Solution • Outcome • Summary

دیتا فابریک

The Data Fabric Architecture • Data Access Policies • Metadata Catalog • Master Data Management • Data Virtualization • Real-Time Processing • APIs • Services • Products • Why Transition from an MDW to a Data Fabric Architecture? • Potential Drawbacks • Summary

دیتا لیک‌هاوس

Delta Lake Features • Performance Improvements • The Data Lakehouse Architecture • What If You Skip the Relational Data Warehouse? • Relational Serving Layer • Summary

مبانی دیتا مش

A Decentralized Data Architecture • Data Mesh Hype • Dehghani’s Four Principles of Data Mesh • Principle #1: Domain Ownership • Principle #2: Data as a Product • Principle #3: Self-Serve Data Infrastructure as a Platform • Principle #4: Federated Computational Governance • The “Pure” Data Mesh • Data Domains • Data Mesh Logical Architecture • Different Topologies • Data Mesh Versus Data Fabric • Use Cases • Summary

آیا باید دیتا مش را بپذیرید؟ افسانه‌ها، نگرانی‌ها و آینده

Myths • Myth: Using Data Mesh Is a Silver Bullet That Solves All Data Challenges Quickly • Myth: A Data Mesh Will Replace Your Data Lake and Data Warehouse • Myth: Data Warehouse Projects Are All Failing, and a Data Mesh Will Solve That Problem • Myth: Building a Data Mesh Means Decentralizing Absolutely Everything • Myth: You Can Use Data Virtualization to Create a Data Mesh • Concerns • Philosophical and Conceptual Matters • Combining Data in a Decentralized Environment • Other Issues of Decentralization • Complexity • Duplication • Feasibility • People • Domain-Level Barriers • Organizational Assessment: Should You Adopt a Data Mesh? • Recommendations for Implementing a Successful Data Mesh • The Future of Data Mesh • Zooming Out: Understanding Data Architectures and Their Applications • Summary

افراد و فرآیندها

Team Organization: Roles and Responsibilities • Roles for MDW, Data Fabric, or Data Lakehouse • Roles for Data Mesh • Why Projects Fail: Pitfalls and Prevention • Pitfall: Allowing Executives to Think That BI Is “Easy” • Pitfall: Using the Wrong Technologies • Pitfall: Gathering Too Many Business Requirements • Pitfall: Gathering Too Few Business Requirements • Pitfall: Presenting Reports Without Validating Their Contents First • Pitfall: Hiring an Inexperienced Consulting Company • Pitfall: Hiring a Consulting Company That Outsources Development to Offshore Workers • Pitfall: Passing Project Ownership Off to Consultants • Pitfall: Neglecting the Need to Transfer Knowledge Back into the Organization • Pitfall: Slashing the Budget Midway Through the Project • Pitfall: Starting with an End Date and Working Backward • Pitfall: Structuring the Data Warehouse to Reflect the Source Data Rather Than the Business’s Needs • Pitfall: Presenting End Users with a Solution with Slow Response Times or Other Performance Issues • Pitfall: Overdesigning (or Underdesigning) Your Data Architecture • Pitfall: Poor Communication Between IT and the Business Domains • Tips for Success • Don’t Skimp on Your Investment • Involve Users, Show Them Results, and Get Them Excited • Add Value to New Reports and Dashboards • Ask End Users to Build a Prototype • Find a Project Champion/Sponsor • Make a Project Plan That Aims for 80% Efficiency • Summary

فناوری‌ها

Choosing a Platform • Open Source Solutions • On-Premises Solutions • Cloud Provider Solutions • Cloud Service Models • Major Cloud Providers • Multi-Cloud Solutions • Software Frameworks • Hadoop • Databricks • Snowflake • Summary