Foreword
Reading headlines like “Facebook moves 30-petabyte Hadoop cluster to new data center” shows that one of the biggest struggles we are facing today is Big Data and its management. Data centric applications, mobile front ends to complex data structures, and serving millions of clients accessing our datasets while handling billions of transactions a day shows that keeping data management simple and easy to handle is a first class problem in modern application development.
Thankfully, tools like Spring Data and it’s many utilities make it easy to access these data sets using whichever flavor of standards best fits our team’s skills and needs. While Java blazed the trail by offering the flexible but consistent JDBC standard, it was the power of Spring that cut out the tedious amounts of boilerplate afforded to us by historical SQL paradigms. This has empowered developers to focus on business logic, scaling requirements, mobile platform support, and other numerous requirements while allowing Spring to handle the chores of managing connections and interacting with various data management technologies. This is analogous to migrating from the manual memory management of coding in C to Java’s sophisticated garbage collection, which removed a whole host of bugs we as developers used to face every day. By reducing the total amount of code we must write to access our own Big ...
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