Map Reduce is dead, long live Spark!

Map Reduce is dead, long live Spark!

That’s the impression I, and I think most people attending the conference, walked away with after Strata NY 2014.  Most of the interesting presentations were centered on Spark.  Only corporate IT presentations about “in progress hadoop implementations” were about Map Reduce.

So who’s working on Spark?  Cool startups and vendors (preparing for enterprise IT departments to move on to Spark in a year or two).

Who’s working on Map Reduce? Corporate IT departments migrating off legacy BI systems onto the promised land of Hadoop (dream come true, or nightmare around the corner, not sure which one it will be for people).

It makes sense. Map Reduce has been tested and is ‘safe’ now for enterprise IT teams to start deploying it into production systems.  Spark is still very new and untested.  Too risky for a Fortune 500 to dive into replacing legacy systems with a still-in-diapers open source software “solution.”  Nonetheless, I am sure every technical worker will be drooling to “prototype” or create proof of concepts with Spark after this conference.

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Reflections on "Hadoop Certification – is it worth it" 18 months later

It has been over a year and half since I took the Cloudera Hadoop Developer Certification course and exam and posted my initial impressions of it on my blog. I have received more comments than I had expected, thank you for reading and sending me comments! There have been a few trends in the comments, some displayed, others kept private. The main ones are:

  1. People really want to get their hands on the Cloudera training materials
  2. People are very eager to get Hadoop jobs
  3. People are trying to transition into Hadoop from different (technical) backgrounds
  4. People want to know if they need to know Java to work with Hadoop
  5. People really want to know if getting a certification in Hadoop will land them a job.

Here is an update to each of these trends:

#1) I cannot share the Cloudera training materials with you, sorry. I wish you the best, but I cannot distribute these materials. They are also pretty old at this point, chances are some of the content is outdated by now. It seems like many of the people asking me for the training materials haven’t picked up any books on the subject at all.  So, please check out the available online resources or pick up some books (Hadoop, the Definitive Guide, comes to mind) .

#2) There is tremendous amount of interest in learning Hadoop (and getting the training materials) in India. If it
is hard to find experienced Hadoop developers in the US right now, I imagine it must be even harder in India (for now, anyway) and there must be many, many job openings right now. I can imagine the outsourcing firms trying to staff up to meet the unmet demand in the US and elsewhere. Almost all the comments and private messages sent to me for training materials were from India. I do not know how much a training course costs in India, but there are plenty of training options, in addition to Cloudera and Hortonworks’ online offerings.

#3) Career switchers (or more accurately, technology-platform-switchers) will need to impress hiring managers with their transferable skill sets and show (not tell) their passion for technology and big data. This is true for any job applicant.

#4) Regarding Java, yes, it is good to know Java to work with Hadoop, but it is not required. You can use other languages, such as python, through the Hadoop Streaming API. To work with big data, python is good language to know anyway (lots of companies are looking with people with linux/python background), so learn python while you are at it (learnpythonthehardway.com). If you know python you will also be able to use Pig to interact with your data. What language you will will be determined by the solution architecture and design. If the company you want to work with has designed a solution with custom coded java map reduce jobs, then you would need to know java. Other places may implement Hadoop Streaming API and use python, so it may be possible to get a job there if you know python.

#5) Having a certification in Hadoop won’t guarantee you a job. Most companies are looking for experienced Hadoop hires, which is hard to do unless they are poaching employees from other Big Data statups or tech firms (Yahoo, Google, etc.). When I interviewed technical job applicants, I was surprised (perhaps I shouldn’t have been) how poorly they interview. So please, please practice your behavioral interviewing skills (“tell me about yourself”, “walk me through your resume”, “tell me about a time you had to solve a difficult problem”, “why do you want this job”, etc.). If someone has 50 certifications and can’t answer these simple questions, I will not consider them for the role. I have heard that some hiring managers consider too many certifications as a cover up for lack of skill (superstar developers don’t bother getting certified / don’t need to be certified). For the rest of us, it can help, but it doesn’t guarantee success. The Cloudera Developer course is a good overview, but for it to be meaningful, you really do need a project to work on. Working on a pet project and being able to share code samples would help set you up for success when interviewing.

As for my own personal experience, I did not get a job working directly with Hadoop following the certification course, but I also was not only considering Hadoop developer roles.  I am now leading a BI implementation project where I interviewed and hired a team of developers and analysts. We are using Pentaho and Vertica (for analytic database) and I have been evangelizing Hadoop and other technologies at my company. I find it humorous when executives say the company needs to do more “big data” or “more Hadoop” without really knowing what it means. The certification course definitely helped me speak more authoritatively about this technology at my company and when networking with others.

Whether or not to take the certification course depends on your individual circumstance. If you are dead-set on getting a job as a Hadoop developer then it may be worth it to you, but make sure to follow up with a personal project to continue learning and practicing. Many people focus on Hadoop, and seem to forget the business applications of using a technology like Hadoop (data science, improved ETL, data processing). Brushing up on those skills and domain knowledge would make you a much more interesting job candidate over all.  Good luck everyone!

Cloudera Hadoop Developer Training: Day 1

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.

Large-Scale Data Storage and Processing for Scientists with Hadoop

Great overview of Hadoop and related “big-data” tools