MongoDB Experience: History

My first exposure to MongoDB was in July 2008 when I was a panelist on “A Panel on Cloud Computing” at the Entrepreneurs Round Table in New York. The panel included a representative from 10gen the company behind the open source database product and at the time Mongo was described as a full stack solution with the database being only one future component.

While I mentioned Mongo again in a blog in Nov 2008, it was not until Oct 6 2009 at the NoSQL event in New York where I saw a more stable product and a revised focus of development just on the database component.

As the moderator for the closing keynote “SQL v NOSQL” panel at Open SQL Camp 2009 in Portland, Oregon I had the chance to discuss MongoDB with the other products in the NoSQL space. Watch Video

In just the past few weeks, 3 people independently have mentioned MongoDB and asked for my input. I was disappointed to just miss the MongoNYC 2010 event.

While I have evaluated various new products in the key/value store and the schemaless space, my curiosity has been initially more with Cassandra and CouchDB .

Follow my journey as I explore in more detail the usage of mongoDB {name: “mongo”, type:”db”} via the mongodb tag on my blog.

Tagged with: Mongodb

NoSQL options

The NoSQL event in New York had a number of presentations on non relational technologies including of Hadoop , MongoDB and CouchDB . Coming historically from a relational background of 20 years with Ingres , Oracle and MySQL I have been moving my focus towards non relational data store.

Producing IQR and Outlier statistics with SQL

The interquartile range (IQR) measures the spread of the middle 50% of a distribution — the distance between the first quartile (Q1) and the third quartile (Q3). Combined with Tukey’s 1.

Producing Mode statistics with SQL

The mode is the value or values that appear most frequently in a dataset. Unlike the mean or median, it applies naturally to categorical and ordinal data — star ratings, product codes, survey responses — and reveals what is most common, not what is average.