Making Sense of NoSQL: A guide for managers and the rest of us
Format: PDF / Kindle (mobi) / ePub
Making Sense of NoSQL clearly and concisely explains the concepts, features, benefits, potential, and limitations of NoSQL technologies. Using examples and use cases, illustrations, and plain, jargon-free writing, this guide shows how you can effectively assemble a NoSQL solution to replace or augment the traditional RDBMS you have now.
About this Book
If you want to understand and perhaps start using the new data storage and analysis technologies that go beyond the SQL database model, this book is for you. Written in plain language suitable for technical managers and developers, and using many examples, use cases, and illustrations, this book explains the concepts, features, benefits, potential, and limitations of NoSQL.
Making Sense of NoSQL starts by comparing familiar database concepts to the new NoSQL patterns that augment or replace them. Then, you'll explore case studies on big data, search, reliability, and business agility that apply these new patterns to today's business problems. You'll see how NoSQL systems can leverage the resources of modern cloud computing and multiple-CPU data centers. The final chapters show you how to choose the right NoSQL technologies for your own needs.
Managers and developers will welcome this lucid overview of the potential and capabilities of NoSQL technologies.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
- NoSQL data architecture patterns
- NoSQL for big data
- Search, high availability, and security
- Choosing an architecture
About the Authors
Dan McCreary and Ann Kelly lead an independent training and consultancy firm focused on NoSQL solutions and are cofounders of the NoSQL Now! Conference.
Table of Contents
- NoSQL: It's about making intelligent choices
- NoSQL concepts
- Foundational data architecture patterns
- NoSQL data architecture patterns
- Native XML databases
- Using NoSQL to manage big data
- Finding information with NoSQL search
- Building high-availability solutions with NoSQL
- Increasing agility with NoSQL
- NoSQL and functional programming
- Security: protecting data in your NoSQL systems
- Selecting the right NoSQL solution
PART 1 INTRODUCTION
PART 2 DATABASE PATTERNS
PART 3 NOSQL SOLUTIONS
PART 4 ADVANCED TOPICS
documents you’re storing. The longer the hash, the lower the odds of a collision. As you add more documents, the chance of a collision increases. Many systems use the MD5 hash algorithm that generates a 128-bit hash string. A 128-bit hash can generate approximately 1038 possible outputs. That means that if you want to keep the odds of a collision low, for example odds of under one in 1018, you want to limit the number of documents you keep to under 1013, or about 10 trillion documents. For most
you have the ability to recognize structures that you’ve seen in the past. For our purposes, we define a data architecture pattern as a consistent way of representing data in a regular structure that will be stored in memory. Although the memory you store data in is usually long-term persistent memory, such as solid state disk or hard drives, these structures can also be stored in RAM and then transferred to persistent memory by another process. It’s also important to understand the difference
specific to our context. Revision control systems are critical for projects that involve distributed teams of developers. For these types of projects, losing code or using the wrong code means lost time and money. These systems use many of the same patterns you see in NoSQL systems, such as distributed systems, document hashing, and tree hashing, to quickly determine whether things are in sync. Early revision control systems (RCSs) weren’t distributed. There was a single hard drive that
using the same tools they use for product sales. This example shows that NoSQL systems may be ideal for some data tasks, but they may not have the same features of a traditional table-centric OLAP system for some analyses. Here, Sally combined parts of a new NoSQL approach with a traditional OLAP tool to get the best of both worlds. 3.9. Summary In this chapter, we reviewed many of the existing features of RDBMSs, as well as their strengths and weaknesses. We looked at how relational
problems associated with most object-relational systems. In short, it was custom designed to meet the needs and demands of the ever-growing ad serving business and in the process turned out to be a good strategy for other problems that don’t have real-time requirements, but want the ability to avoid the complex and slow object-relational mapping problems of traditional systems. As well as being used as a basis for banner ad serving, MongoDB can be used in some of the following use cases: