Making Sense of NoSQL – A balanced, well-written overview – #bigdata #bookreview

Making Sense of NoSQL

A Guide for Managers and the Rest of Us
Dan McCreary and Ann Kelly
(Manning, paperback)

This is NOT a how-to guide for learning to use NoSQL software and build NoSQL databases. It is a meaty, well-structured overview aimed primarily at “technical managers, [software] architects, and developers.” However, it also is written to appeal to other, not-so-technical readers who are curious about NoSQL databases and where NoSQL could fit into the Big Data picture for their business, institution, or organization.

Making Sense of NoSQL definitely lives up to its subtitle: “A guide for managers and the rest of us.”

Many executives, managers, consultants and others today are dealing with expensive questions related to Big Data, primarily how it affects their current databases, database management systems, and the employees and contractors who maintain them. A variety of  problems can fall upon those who operate and update big relational (SQL) databases and their huge arrays of servers pieced together over years or decades.

The authors, Dan McCreary and Ann Kelly, are strong proponents, obviously, of the NoSQL approach. It offers, they note, “many ways to allow you to grow your database without ever having to shut down your servers.” However, they also realize that NoSQL may not a good, nor affordable, choice in many situations. Indeed, a blending of SQL and NoSQL systems may be a better choice. Or, making changes from SQL to NoSQL may not be financially feasible at all. So they have structured their book into four parts that attempt to help readers “objectively evaluate SQL and NoSQL database systems to see which business problems they solve.”

Part 1 provides an overview of NoSQL, its history, and its potential business benefits. Part 2 focuses on “database patterns,” including “legacy database patterns (which most solution architects are familiar with), NoSQL patterns, and native XML databases.” Part 3 examines “how NoSQL solutions solve the real-world business problems of big data, search, high availability, and agility.” And Part 4 looks at “two advanced topics associated with NoSQL: functional programming and system security.”

McCreary and Kelly observe that “[t]he transition to functional programming requires a paradigm shift away from software designed to control state and toward software that has a focus on independent data transformation.” (Erlang, Scala, and F# are some of the functional languages that they highlight.) And, they contend: “It’s no longer sufficient to design a system that will scale to 2, 4, or 8 core processors. You need to ask if your architecture will scale to 100, 1,000, or even 10,000 processors.”

Meanwhile, various security challenges can arise as a NoSQL database “becomes popular and is used by multiple projects” across “department trust boundaries.”

Computer science students, software developers, and others who are trying to stay knowledgeable about Big Data technology and issues should also consider reading this well-written book.

Si Dunn

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Spring Data: Modern Data Access for Enterprise Java – #java #bookreview

Spring Data: Modern Data Access for Enterprise Java
Mark Pollack, Oliver Gierke, Thomas Risberg, Jonathan L. Brisbin and Michael Hunger
(O’Reilly, paperbackKindle)

Big Data keeps getting wider and deeper by the second. And so do the demands for analyzing and profiting from all of those piled-up terabytes.

Meanwhile, the once whiz-bang technology known as the relational database is having a very hard time keeping pace. The sheer amount of data that companies now gather, store, access, and analyze is pushing traditional relational databases to the breaking point.

Many Java developers who are trying to keep these overloaded systems held together with baling wire, also are starting to learn to work with some of the “alternative data stores that are being used in mission-critical enterprise applications,” the authors of Spring Data point out.

A lot of data now is being stored elsewhere and not in relational databases. Yet companies cannot abandon what they have already gathered and invested heavily to access. So they need to keep using and supporting their relational databases, plus some newer, faster, more voracious solutions lumped under the heading “NoSQL databases,” (even though you can query them).

In “the new data access landscape,” the authors note: “there is a revolution taking place, which for data geeks is quite exciting. Relational databases are not dead; they are still central to the operations of many enterprises and will remain so for quite some time. The trends, though, are very clear: new data access technologies are solving problems that traditional relational databases can’t, so we need to broaden our skill set as developers and have a foot in both camps.”

They add: “The Spring Framework has a long history of simplifying the development of Java applications, in particular for writing RDBMS-based data access layers that use Java database connectivity (JDBC) or object-relational mappers.”

Their new book “is intended to give you a hands-on introduction to the Spring Data project, whose core mission is to enable Java developers to use state-of-the-art data processing and manipulation tools but also use traditional databases in a state-of-the-art manner.”

They have organized their 288-page book into six parts and 14 chapters:

Part I – Background

  • Chapter 1 – The Spring Data Project
  • Chapter 2 – Repositories: Convenient Data Access Layers
  • Chapter 3 – Type-Safe Querying Using Querydsl

Part II – Relational Databases

  • Chapter 4 – JPA Repositories
  • Chapter 5 – Type-safe JDBC Programming with Querydsl SQL

Part III – NoSQL

  • Chapter 6 – MongoDB: A Document Store
  • Chapter 7 – Neo4j: A Graph Database
  • Chapter8 – Redis: A Key/Value Store

Part IV – Rapid Application Development

  • Chapter 9 – Persistence Layers with Spring Roo
  • Chapter 10 – REST Repository Exporter

Part V – Big Data

  • Chapter 11 – Spring for Apache Hadoop
  • Chapter 12 – Analyzing Data with Hadoop
  • Chapter 13 – Creating Big Data Pipelines with Spring Batch and Spring Integration

Part 5 – Data Grids

  • Chapter 14 – GemFire: A Distributed Data Grid

“Many of the values that have made Spring the preferred platform for enterprise Java developers deliver particular benefit in a world of fragmented persistence solutions,” states Ron Johnson, creator of Spring Framework. Writing in the book’s foreword, he notes: “Part of the value of Spring is how it brings consistency (without descending to a lowest common denominator) in its approach to different technologies with which it integrates.

“A distinct ‘Spring way’ helps shorten the learning curve for developers and simplifies code maintenance. If you are already familiar with Spring, you will find that Spring Data eases your exploration and adoption of unfamiliar stores. If you aren’t already familiar with Spring, this is a good opportunity to see how Spring can simplify your code and make it more consistent.”

Spring Data definitely is not light reading, but it is well-written, and provides a good blending of procedures, steps, explanations, code samples, screenshots and other illustrations.

Si Dunn