I’ve just completed my latest book, this time looking at the development side of using Couchbase Server for building applications. The book goes through the basics of the Couchbase Server data store, the mechanics of storing and using data, the API and operations available, and a quick overview of the different client libraries available for building applications.
With the core details out of the way, I move on to building a sample application using the PHP client library as the base, showing the different operations in context, and then looking at the indexing and query system for searching for data from Couchbase Server.
As databases evolve, learning how to get the best out of the different solutions out there is the key to understanding and extracting the data in the way you need from your required data store. Document databases, like MongoDB, CouchDB, Couchbase Server and many others provide a completely different model and set of problems for interfacing and extracting data.
You need to be able to understand your structure, how you can query the information, and how to perform different data mining techniques on what is very obviously a completely different structure of information.
In this article, I try to take you through the basics of data mining when using a document database.
To continue from where my last blog left off, I’ve written a second piece that tries to cover some of the more complex solutions to the problems of querying and extracting data using the Views system within Couchbase Server.
Before moving to Couchbase and working with NoSQL technology I had for years been a MySQL user. Making that leap from MySQL to NoSQL requires a number of changes, not least of which to the way you structure your data and then query it.
An article covering more of the detail is available here: