Big Data is all the rage nowadays, and there are a ton of tools out there to help you gather and aggregate it. These articles are about the tools, techniques, and concepts necessary to separate the wheat from the chaff about big data.
Last week I attended Oracle OpenWorld 2014, and it was an outstanding event filled with great people, awesome sessions, and a few outstanding notable experiences. Personally I thought the messaging behind the conference itself wasn’t as amazing and upbeat as OpenWorld 2013, but that’s almost to be expected. Last year
MapReduce with Hadoop Streaming in bash – Bonus! To conclude my three part series on writing MapReduce jobs with shell script for use with Hadoop Streaming, I’ve decided to throw together a video tutorial on running the jobs we’ve created in Oozie, a workflow editor for Hadoop that allows jobs
In our first MapReduce with Hadoop Streaming in bash article, we took a collection of Stephen Crane poems and used a MapReduce job to calculate ‘term frequency’–meaning we counted the number of times each word in the collection appeared in the collection. In the second part, we calculated ‘document frequency’
In MapReduce with Hadoop Streaming in bash – Part 1 we found the ‘term frequency’ of words within a collection of documents. For the documents I chose 8 Stephen Crane poems, and our bash Map and Reduce jobs tokenized the words and found their frequency among the entire set. The
So to commemorate my recent certification and because my Java absolutely sucks, I decided to do a common algorithm using Hadoop Streaming. Hadoop Streaming Hadoop Streaming allows you to write MapReduce code in any language that can process stdin and stdout. This includes Python, PHP, Ruby, Perl, bash, node.js, and
Taking the Cloudera Developer Training for Apache Hadoop had many rewards — one of which was a free voucher to take the CCD-410 Exam (normally $295) which you must pass to get CCDH certified. I’m not sure if that’s a Cloudera University or Global Knowledge thing, but either way it
This will be a short(ish) post, as my brain is relatively fried from the obscene amount of knowledge imparted by the Hadoop class (and it’s Friday so I’m allowed to be lazy nanny nanny boo boo). To be honest, this was probably the most fun day of class. While the
Cloudera Developer Training for Apache Hadoop is almost over, and I’m somewhat sad that my Hadoopin’ days are nearly done–in the classroom at least. However, the breadth of this training has been great and I can definitely say I’ve gotten my (company’s) money’s worth. Being that I’m three days in,
Yesterday I completed the second day of Cloudera Developer Training for Apache Hadoop. While the first day focused on Hadoop core technology like HDFS, the second day was all about MapReduce. That means it was the day that whole ‘developer’ thing was thrown into sharp relief. I’ve been a DBA
What’s the best way to follow up a week of Oracle OpenWorld? Cloudera Developer Training for Apache Hadoop of course. So today I had my first day. I won’t detail the course itself (though I hope there will be many Hadoop posts to come). But I would like to share
