Cloudera Hadoop Developer Training: First Impressions

Just wrapped the first day of Cloudera Hadoop Developer Training… so far so good! Training lasts 4 days, with the option to take the Certification Exam within 30 days of course completion. Apparently the exam policy is changing in the next couple of months. Right now, the exam is a timed multiple-choice, open book, un-proctored exam that is taken online. Soon it will become a Pearson-administered exam, which means, registering in advance for exams, taken in a highly supervised test environment, and I assume, a closed book format, but not sure. I am taking the course in Columbia Maryland, with Mark Fei as the instructor. He’s great! Passionate and knowledgeable about the material, good course leader and instructor. He’s probably taught this class countless times, but he still has plenty of enthusiasm and interest in answering questions.

Day 1 included a high level overview of Hadoop ecosystem and the Map Reduce algorithm. We had a chance to do hands-on labs in the afternoon and ran our first Map Reduce (MR) algorithms in Java, a word count map reduce, the “hello world” example of Hadoop. Next, we wrote our own average word length map reduce algorithm. The course officially does not require Java knowledge, but it definitely helps knowing how to code in Java or another object oriented language. We discussed how to write Map Reduce algorithms in Python, Perl, even UNIX shell scripts via the Hadoop Streaming API. Mark gave us an overview of the Hadoop ecosystem and discussed how Hive, Pig, Sqoop, and Oozie work with Hadoop Map Reduce in the production environment.

Cloudera Hadoop Developer Training: Is it Worth it?

My biggest takeaway today was the anecdotal observation that 75% of all Hadoop Map Reduce jobs in production are likely invoked via Hive and Pig. Hive provides a “SQL-lite” front end interface to Map Reduce. HiveQL statements are translated into Map Reduce jobs behind the scenes, allowing business analysts to easily query the Hadoop cluster. Pig provides a scripted language interface to Hadoop, allowing users to write more complex queries (in “PigLatin”) without having to write Java. At this point in the class, I asked myself, wow, if 75% of all Map Reduce is being done by Hive or Pig, why am I here taking this Java course? Is the Cloudera Developer course still worth it for the “typical user?” I think for most users, especially those with solid SQL backgrounds and aren’t all that concerned with the inner workings of Hadoop, learning and using Hive is probably good enough. I like “getting under the hood” and truly understanding what’s going on, knowing how to optimize queries, and being able how to deal with the more challenging edge cases. Also, I am interested in learning how to use machine language algorithms with Hadoop. So I think there is still value in learning the edge cases and complexity.

Another key takeaway was getting a good sense of how I could re-architect a production environment using Hadoop and its related tools. At Vostu we were looking at ways to improve the ETL process for our analytics database environment. We were working with Large Data, and our ETL process, which had not been updated in a long time, was showing its age and its limits in scalability as we added more games and experienced viral user growth. In order to leverage Hadoop’s full potential, it seemed to me that an ETL redesign needed to be on the Production team’s radar screen also, not just the Analytics team. It was good hearing Mark’s insights on how other Hadoop practitioners dealt with the very real political and organizational challenges of introducing Hadoop to their respective companies. In a world where data center managers associate big storage with big storage appliances (i.e., SAN or NAS installations), Hadoop relies on commodity hardware with direct storage. Data center managers may not “get it,” and may be resistant, especially since in many Infrastructure teams, you have separate DBA and Storage teams – so who would be the owner of an integrated DB + Storage solutions? Selling and implementing Hadoop to an organization can represent an up-hill challenge to less-than-innovative IT organizations.

Cloudera Hadoop Developer Training: Event Details

The training location, Bridge Education, has reasonable facilities. After a while I noticed all the Sun Server posters (server p0rn, anyone?) in the hallways, classrooms, kitchen pantry, basically everywhere. I hadn’t realized what a big deal Sun Java J2EE Certifications were; that’s the only connection I could make to the wall art. Lunch was provided, Chipotle on first day, so we didn’t need to waste time looking for a place to eat.

Cloudera provides electronic copies of all course materials, including a VMWare Virtual Machine running CentOS with a Hadoop instance running in pseudo-distributed mode. This was pretty cool: it runs separate daemons for HDFS Master/Slave Nodes, and Hadoop Name / Data Nodes, etc., allowing one to fully experience how to get data in/out of a Hadoop cluster without needing to actually configure a multi-node cluster environment. The lecture notes, a 500-page PowerPoint PDF file, is downloaded from the training site. The best part is the fully configured VM image, saving me tons of time downloading and installing this framework for testing at home. It is very similar to the Cloudera image available on the web, so overall, the best way to test Hadoop and run the sample Map Reduce jobs would be through a VM image.