Silly article on MapReduce vs Map Reduce

_posts/2016-06-21-mapreduce-or-map-reduce.md

@@ -0,0 +1,43 @@
+---
+title: Is it 'MapReduce' or 'Map Reduce'?
+date: 2016-06-30
+subject: hadoop
+description: Confused about whether Map Reduce is one word or two? Let me settle this once and for all.
+layout: post
+published: true
+image:
+  url: /img/question.jpg
+  author:
+    name: Beatnik Photos
+    url: https://www.flickr.com/photos/dharmabum1964/3108162671
+
+---
+
+MapReduce is a data processing methodology made popular by [Hadoop](http://hadoop.apache.org). It describes a way that multiple computational units can work together to process a large scale dataset whilst acting independantly and not depending on one another.
+
+Should you call this technology 'MapReduce' or 'Map Reduce'? It's a question that is trivial, but common. Personally I'm very unreliable with how I describe the technology, [sometimes I write 'MapReduce'](http://blog.matthewrathbone.com/2016/01/05/experts-and-mapreduce.html), and [sometimes I write 'Map Reduce'](http://blog.matthewrathbone.com/2013/05/31/hadoop-resources-books.html).
+
+The short version is that the correct spelling is 'MapReduce'. That is - all one word with R capitalized. You shouldn't write 'Map Reduce' or 'Map/Reduce'.
+
+## The case for MapReduce vs Map Reduce
+
+Google's seminal paper from 2004 is titled [*MapReduce: Simplified Data Processing on Large Clusters*](http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf). They're very consistent about using *MapReduce* to describe the concept and nowhere in the paper do they split this into two words.
+
+This is backed up by Google search traffic which shows *MapReduce* has a clear lead.
+
+![Google Trends](/img/mapreduce.png)
+screenshot from [Google Trends](https://www.google.com/trends/explore#q=MapReduce%2C%20Map%20Reduce&cmpt=q&tz=Etc%2FGMT%2B5)
+
+The [Apache Hadoop website](http://hadoop.apache.org) and big Hadoop vendors like Cloudera and Hortonworks refer to it as *MapReduce* also.
+
+## It's not all that clear
+
+However, outside of the Hadoop ecosystem naming is less clear. MongoDB has it's own MapReduce implementation, but [it is referred to as 'Map-reduce'](https://docs.mongodb.com/manual/core/map-reduce/) (they don't even capitalize the R! \*Gasps\*).
+
+Even back in Hadoop-land not all content has settled on the *MapReduce*. There are [several](http://ksat.me/map-reduce-a-really-simple-introduction-kloudo/) [examples](https://www.hackerrank.com/domains/distributed-systems/mapreduce-basics) of places where folks are confused, or even [use several different spellings](https://www.linkedin.com/pulse/map-reduce-tutorial-gives-brief-overview-application-agrawal).
+
+## Wrap-up
+
+It doesn't *really* matter of couse, but now you know -- one *MapReduce* to rule them all.
+
+While you're here, check out [my guide to MapReduce frameworks](http://blog.matthewrathbone.com/2013/01/05/a-quick-guide-to-hadoop-map-reduce-frameworks.html)

_posts/tumblr/2013-01-05-a-quick-guide-to-hadoop-map-reduce-frameworks.md

@@ -1,6 +1,6 @@
 ---
 layout: post
-title: "Hadoop Map-Reduce Framework Tutorials with Examples"
+title: "Hadoop MapReduce Framework Tutorials with Examples"
 subject: hadoop
 description: "A constantly expanding list of 12+ hadoop frameworks, with code examples and documentation links"
 tags: 
@@ -17,7 +17,7 @@ published: true
 
 **Updated October 2015** Full sample code is available for many frameworks, see the list [at the bottom of the article](#updates)
 
-There are a lot of frameworks for writing map-reduce pipelines for Hadoop, but
+There are a lot of frameworks for writing MapReduce pipelines for Hadoop, but
 it can be pretty hard to navigate everything to get a good sense of what
 framework you should be using. I felt very overwhelmed when I started working
 with Hadoop, and this has only gotten worse for newcomers as the number of
@@ -31,7 +31,7 @@ Generally speaking, the goal of each framework is to make building pipelines
 easier than when using the basic map and reduce interface provided by hadoop-
 core. This usually means the frameworks do not require you to write these
 functions at all, but something more high-level that the framework can
-'compile' into a pipeline of map-reduce jobs. This is particularly true for
+'compile' into a pipeline of MapReduce jobs. This is particularly true for
 the higher level frameworks (such as hive), which don't really require any
 knowledge of programming to operate.
 
@@ -106,7 +106,7 @@ please tweet me if I have missed any: [@rathboma](http://twitter.com/rathboma)
 ## Framework Walkthroughs ## {#walkthrough}
 
 I will create a separate article for each framework ( [current articles listed here](#updates) ) in which I will build a
-small map-reduce pipeline to do the following:
+small MapReduce pipeline to do the following:
 
 Given two (fake) datasets:
 
@@ -127,7 +127,7 @@ example.
 ## My Commonly used Frameworks
 
 * Hive -- Hive is amazing because anyone can query the data with a little knowledge of SQL. Hook it up to a visual query designer and you don't even need that.
-* Pig -- the perfect framework for prototyping and quick-investigation. It's a simple scripting language with a bunch of powerful map-reduce specific features.
+* Pig -- the perfect framework for prototyping and quick-investigation. It's a simple scripting language with a bunch of powerful MapReduce specific features.
 * Scoobi -- I use this a lot to build pipelines in Scala because it's very functional, and in many way's you just treat the data like a regular list, which is great.
 * Raw Map/Reduce -- Sometimes I like to program directly to the API, especially when doing something mission critical. I also find the individual map and reduce functions easier to test.