It is precisely in this context that I’ve decided to launch this new endeavor and write a book that offers a practical perspective on those new data platform deployments that have been successful, as well as practical use cases and plausible design blueprints for your organization or data management project. The information, insight, and guidance that I will provide is based on lessons I’ve learned through research projects and other efforts examining robust and solid data management platform solutions for many organizations.
The resources for this project will require crowdfunding efforts, and here is where your collaboration will be extremely valuable. There are several ways in which you can participate:
- Participating in our Data Management Platforms survey to obtain a nice discount right off the bat)
- Pre-ordering the book (soon, I’ll provide you with details on how to pre-order your copy, but in the meantime, you can show your interest by signing up at the link below)
- Providing us with information about your own successful enterprise use case, which we may use in the book.
To let us know which of these options best fits with your spirit of collaboration, and to receive the latest updates on this book, as well as other interesting news, you just need to sign up to our email list here. Needless to say, the information you provide will be kept confidential and used only for the purpose of developing this book.
In the meantime, I’d like to leave you with a brief synopsis of the contents of this book, with more details to come in the near future. More details will be added to keep you posted on the progress of the book.
New Data Management Platforms
Discovering Architecture Blueprints
What Is This Book About?
This book is the result of a comprehensive study into the improvement, expansion, and modernization of different types of architectures, solutions, and platforms to address the need for better and more effective ways of dealing with increasing and more complex volumes of data.
In conducting his research for the book, the author has made every effort to analyze in detail a number of successful modern data management deployments as well as the different types of solutions proposed by software providers, with the aim of providing guidance and establishing practical blueprints for the adoption and/or modernization of existing data management platforms. These new platforms have the capability of expanding the ability of enterprises to manage new data sources—from ingestion to exposure—more accurately and efficiently, and with increased speed.
The book is the result of extensive research conducted by the author examining a wide number of real-world, modern data management use cases and the plethora of software solutions offered by various software providers that have been deployed to address them. Taking a software vendor‒agnostic viewpoint, the book analyzes what companies in different business areas and industries have done to achieve success in this endeavor, and infers general architecture footprints that may be useful to those enterprises looking to deploy a new data management platform or improve an already existing one.
Who Is This Book For?
This book is intended for both business and technical professionals in the area of information technology (IT). These roles would include chief information officers (CIOs), chief technology officers (CTOs), chief financial officers (CFOs), data architects, and data management specialists interested in learning, evaluating, or implementing any of the plethora of new technologies at their disposal for modernizing their existing data management frameworks.
The book is also intended for students in the fields of computer sciences and informatics interested in learning about new trends and technologies for deploying data architecture platforms. It is not only relevant for those individuals considering pursuing a big data/data management‒related career, but also for those looking to enrich their analytics/data sciences skills with information about new platform technologies.
This book is also relevant for:
- Professionals in the IT market who would like to enrich their knowledge and stay abreast of developments in information management.
- Entrepreneurs who would like to launch a data management platform start-up or consultancy, enhancing their understanding of the market, learning about some start-up ideas and services for consultants, and gaining sample business proposals.
- Executives looking to assess the value and opportunities of deploying and/or improving their data management platforms.
- Finally, the book can also be used by a general audience from both the IT and business areas to learn about the current data management landscape and technologies in order to acquire an informed opinion about how to use these technologies for deploying modern technology data management platforms.
What Does This Book Cover?
The book covers a wide variety of topics, from a general exploration of the data management landscape, to a more defined revision of the topics, including the following:
- The evolution of data management
- A comprehensive introduction to Big Data, NoSQL, and analytics databases
- The emergence of new technologies for faster data processing—such as in-memory databases, data streaming, and real-time technologies—and their role in the new data management landscape
- The evolution of the data warehouse and its new role within modern data management solutions
- New approaches to data management, such as data lakes, enterprise data hubs, and alternative solutions
- A revision of the data integration issue—new components, approaches, and solutions
- A detailed review of real-world use cases, and a suggested approach to finding the right deployment blueprint
How Is the Book Structured?
The book is divided into four comprehensive parts that offer a historical perspective and the ground basis for the development of management platforms and associated concepts, and the analysis of real-world cases of modern data management frameworks toward the establishment of potential development blueprints for deployment.
- Part I. A brief history of diverse data management platform architectures, and how their evolution has set the stage for the emergence of new data management technologies.
- Part II. The need for and emergence of new data management technologies such as Big Data, NoSQL, data streaming, and real-time systems in reshaping existing data management infrastructures.
- Part III. An in-depth exploration of these new technologies and their interaction with existing technologies to reshape and create new data management infrastructures.
- Part IV. A study of real-world modern data management infrastructures, along with a proposal of a concrete and plausible blueprint.
The following is a general outline of the book:
<Table of Content>
Part I. Brief History of Data Management Platform Architectures
Chapter 1. The Never-Ending Need to Manage Data
Chapter 2. The Evolution of Structured Data Repositories
Chapter 3. The Evolution of Data Warehouse as the Main Data Management Platform
Part II. The Need for and Emergence of New Data Management Technologies
Chapter 4. Big Data: A Primer
Chapter 5. NoSQL: A Primer
Chapter 6. Need for Speed 1: The Emergence of In-Memory Technologies
Chapter 7: Need for Speed 2: Events, Streams, and the Real-Time Paradigm
Chapter 8 The Role of New Technologies in Reshaping the Analytics and Business Intelligence Space
Part III. New Data Management Platforms: A First Exploration
Chapter 9. The Data Warehouse, Expanded and Improved
Chapter 10. Data Lakes: Concept and Approach
Chapter 11. Data Hub: Concept and Approach
Chapter 13. Analysis of Alternative Solutions
Chapter 12. Data Lake vs. Data Hub: Key Differences and Considerations
Chapter 13. Considerations on Data Ingestion, Integration, and Consolidation
Part IV. Studying Plausible New Data Management Platforms
Chapter 14. Methodology
Chapter 15. Data Lakes
Sub-Chapter 15.1. Analyzing three real-world uses cases
Sub-Chapter 15.2 Proposing a feasible blueprint
Chapter 16. Data Hubs
Sub-Chapter 16.1. Analyzing three real-world uses cases
Sub-Chapter 16.2. Proposing a feasible blueprint
Chapter 17. Summary and Conclusion
Summary and Conclusions
Appendix A. The Cloud Factor: Data Management Platforms in the Cloud
Appendix B. Brief Intro into Analytics and Business Intelligence with Big Data
Appendix D. Brief Intro into Virtualization and Data Integration
Appendix E. Brief Intro into the Role of Data Governance in Big Data & Modern Data Management Strategies
Appendix F. Brief Intro into Analytics and Business Intelligence with Big Data
About the Author
Jorge Garcia is an industry analyst in the areas of business intelligence (BI) and data management. He’s currently a principal analyst with Technology Evaluation Centers (TEC).
His experience expands for more than 25 years in all phases of application development, database, data warehouse (DWH) and analytics and BI solution design, including more than 15 years in project management, covering best practices and new technologies in the BI/DWH space.
Prior to joining TEC, he was a senior project manager and senior analyst developing BI, DWH, and data integration applications using solutions such as Oracle, SAP, Informatica, IBM, Teradata, among others. Garcia also worked on projects related to the implementation of data management solutions for the private and public sectors including banking, insurance, retail, and services.
A proud member of the Boulder BI Brain Trust, Garcia also makes frequent public speaker appearances, and is an educator and influencer in different topics related to data management.
When not busy researching, speaking, consulting, and mingling with people in this industry, Garcia finds solace as an avid reader, music lover, and soccer fan, as well as proud father "trying" to raise his three lovely kids while his wife tries to re-raise him.