By Asya Kamsky (@asya999)

I've been involved with databases since the early 1990's when I "accidentally" got a job with a small database company. For the next two decades, databases were synonymous with SQL in my mind, until someone asked me what I thought about these new "No SQL" databases, and MongoDB in particular. I tried MongoDB for a small project I was doing on the side, and the rest, as they say, is history.

When I joined the company that created MongoDB in early 2012, the query language was simple and straightforward but didn't include options for easy data aggregation, because the general advice was "store the data the way you expect to access the data", which was a fantastic approach for fast point queries. As time went on, though, it became clear that sometimes you want to answer questions that you didn't know you'd have when you were first designing the application, and the options for that within the database itself were limited. Map-Reduce was complicated to understand and get right, and required writing and running JavaScript, which was inefficient. This led to a new way to aggregate data natively in the server, which was called "The Aggregation Framework". Since the stages of data processes were organized as a pipeline (familiarly evoking processing files on the Unix command line, for those of us who did such things a lot) we also referred to it as "The Aggregation Pipeline". Very quickly "Agg" became my favorite feature for its flexibility, power and ease of debugging.

We've come a long away in the last nine years, starting with just seven stages and three dozen expressions operating on a single collection, to where we are now: over thirty stages, including special stages providing input to the pipeline, allowing powerful output from the pipeline, including data from other collections in a pipeline, and over one hundred and fifty expressions, available not just in the aggregation command but also in queries and updates.

The nature of data is such that we will never know up-front all the questions we will have about it in the future, so being able to construct complex queries (aka aggregations) about it is critical to success. While complex data processing can be performed in any programming language you are comfortable with, being able to analyze your data without having to move it from where it's currently stored provides a tremendous advantage over exporting and loading the data elsewhere just to be able to use it for your analytics.

For years, I've given talks about the power of the Aggregation Pipeline, answered questions from users about how to do complex analysis with it, and frequently fielded requests for a comprehensive "Aggregation Cookbook". Of course it would be great to have a repository of "recipes" with which to solve common data tasks that involve more than a single stage or expression combination, but it's hard to find the time to sit down and write something like that. This is why I was so stoked to see that my colleague, Paul Done, had just written this book and laid the foundation for that cookbook.

I hope you find this collection of suggestions, general principles, and specific pipeline examples useful in your own application development and I look forward to seeing it grow over time to become the cookbook that will help everyone realize the full power of their data.