From my comments on individual chapters, it is probably no surprise that I really enjoyed reading this book. It hits an important market, because there is so much buzz and hype about NoSQL systems, yet so little good and current information about them as a whole, and comparing them and contrasting them, especially with the relational model.
This book is just the right length too. Too big, and too few people would read it and it would risk losing people in the detail weeds. Too small, and it would brush over the technical depth needed to appreciate the technical tradeoffs in various approaches. It covers all the key topics, including many that are missed in other books of its type (like security, structured selection, agility, object persistence, and taxonomy), and does so both clearly and concisely.
This is the first book I recommend to people seeking information about this topic. This might be all a person needs, or it can be a gateway to other more detailed resources, covering specific systems.
I only have a few criticisms. I think it emphasizes the document store more heavily than the other models, probably a reflection of the authors' experience. This makes it great for someone (like me) who is new to that model, but at the expense of balance. I think that it could more clearly contrast the Hadoop MapReduce key value model with the key value stores (like Amazon S3), and more clearly position Hadoop and NoSQL.
The authors face the daunting task of keeping this material fresh and current. Since its publication, there is growing interest in in-memory databases, like SAP HANA and Apache Spark, and more diverse use cases in machine learning, data science, and scientific computing to consider. Hadoop YARN will usher in a new era of even more diverse applications, hopefully playing well together. The gold rush and increasingly demanding use cases will bring in even more new technologies to consider. I hope that new editions can keep pace with these changes.
This book is just the right length too. Too big, and too few people would read it and it would risk losing people in the detail weeds. Too small, and it would brush over the technical depth needed to appreciate the technical tradeoffs in various approaches. It covers all the key topics, including many that are missed in other books of its type (like security, structured selection, agility, object persistence, and taxonomy), and does so both clearly and concisely.
This is the first book I recommend to people seeking information about this topic. This might be all a person needs, or it can be a gateway to other more detailed resources, covering specific systems.
I only have a few criticisms. I think it emphasizes the document store more heavily than the other models, probably a reflection of the authors' experience. This makes it great for someone (like me) who is new to that model, but at the expense of balance. I think that it could more clearly contrast the Hadoop MapReduce key value model with the key value stores (like Amazon S3), and more clearly position Hadoop and NoSQL.
The authors face the daunting task of keeping this material fresh and current. Since its publication, there is growing interest in in-memory databases, like SAP HANA and Apache Spark, and more diverse use cases in machine learning, data science, and scientific computing to consider. Hadoop YARN will usher in a new era of even more diverse applications, hopefully playing well together. The gold rush and increasingly demanding use cases will bring in even more new technologies to consider. I hope that new editions can keep pace with these changes.
Rating: 5/5 Stars.