#nosql tutorial

A two-part MongoDB tutorial for PHP developers: first part covering installation and the second giving a brief intro to MongoDB PHP API for queries and indexes. While not the tutorial that will get you up and running, it’s still a good resource for beginners.

A slide deck by Pierre De Wilde with a short theoretical intro to property graphs and graph databases and an extensive set of examples of manipulating and traversing graph data with Gremlin. Good reference material.

A walk in graph databases v1.0 by Pierre De Wilde

A couple of recent books that I’ll be adding to the list of NoSQL books:

Mathias Meyer’s Riak Handbook. You can get an idea of the book by checking Consistent Hashing Explained: The What and the Why, the free sample chapter, and the table of contents.

Karl Seguin’s The Little Redis Book. Karl is at the second free NoSQL book after the The Little MongoDB Book.

A very practical example of how Hive and Hadoop could deliver value when applied to clickstreams, the most common data for each web property:

Hadoop, Hive, and related tech­nologies are formi­dable tools for unlocking value from data. […] Retention measure­ments are partic­u­larly signif­icant because they paint a detailed picture about the overall stick­iness of a product across the entire userbase.

The same clickstream data can be used to calculate visitors’ conversion with the Bayesian discriminant using Hadoop.

Steve Salevan’s 7 step guide to setting up, compiling, deploying, and running a basic MapReduce job.

When Google unveiled its MapReduce algorithm to the world in an academic paper in 2004, it shook the very foundations of data analysis. By establishing a basic pattern for writing data analysis code that can run in parallel against huge datasets, speedy analysis of data at massive scale finally became a reality, turning many orthodox notions of data analysis on their head.

Google published the paper. Yahoo open sourced this. And Amazon is offering (unlimited) resources.

Update: The Hacker News thread where the main question answered is what other corporations are using MapReduce (besides the Internet companies). The answer is unfortunately extremely short: too many to be able to enumerate them all.

Inspired by the MongoDB MapReduce translated to SQL and Neo4j Cypher Querying for SQL People, MarkLogic’s Jason Hunter and Eric Bloch put together a page mapping SQL terms and queries to MarkLogix terms and XQuery queries respectively.

Here is how SQL statements translate to MarkLogic XQuery expressions:

A 12 minutes screencast introducing the basics of Couchbase Mobile for Android applications:

[…] we’ve built up a Doctor Who data model that shows how Neo4j can be used to address several different data and domain concerns. For example, part of the dataset includes timeline data, comprising seasons, stories and episodes; elsewhere we’ve social network-like data, with characters connected to one another through being companions, allies or enemies of the Doctor. It’s a messy and densely-connected dataset – much like the data you might find in a real-world enterprise. Some of it is of high quality, some of it is lacking in detail. And for every seeming invariant in the domain, there’s an awkward exception – again, much like the real world.

Ian Robinson’s post will walk you through the details of structuring and querying the data with Neo4j. The complete source code is available on GitHub.