Hadoop has become synonymous to Big Data. Oracle has release the latest standard to Java EE stack: Batch Processing JSR 352. Batch processing has been around for decades and there are many Java framework already available such Spring Batch. This talks provides a perspective about Hadoop and JSR352. Knowing when to use or the other or both together.
Hadoop Developer – WordCount tutorial using Maven and NetBeans 7.3RC2
I have adapted the WordCount tutorial to Maven based development as this probably the most popular way to develop in companies. I am not going to rewrite how the WordCount tutorial works but it aims to get you up-and-running with Hadoop development pretty quickly.
I used NetBeans 7.3RC2 because of its integration with Maven but feel free to use an IDE of your choice. I am also using Ubuntu 12.10 64Bit as a development enviroment. I installed the Hadoop debian distribution package.
Warning
When running your WordCount application, Hadoop might throw an out of memory exception, this is because the default settings are -Xmx100m. Apache website mentioned how to fix it but it’s not relevant if you install it using the Debian distribution. Here is a quick solution, open the /usr/bin/hadoop (changing this file has no effect and doesn’t fix the problem /etc/hadoop/hadoop-env.sh):
- set your JAVA to the actual JVM path that you want to use.
- set JAVA_HEAP_MAX to increase the available memory to the applications i.e. -Xmx3000m
Here are the steps to creating the WordCount tutorial in NetBeans:
- Create a new Maven based Java project
- NetBeans will create an App.java class, you can rename it to WordCount or leave it as it doesn’t affect the outcome of the tutorial. I will refer to the main class as App.java.
- Add the Hadoop dependencies, they are available in Maven Central. I used the hadoop-core.1.1.1 for this tutorial.
- Important: Maven doesn’t package dependencies when building application unless you are working with a “war” project where it will create a lib folder. In order to make sure that our libraries are available to the our program when packaged, we need to add the maven-assembly-plugin to our pom.xml. We also declare a our “Main” class which will be used to execute the program.
- Open App.java (or whatever you have renamed it to) and write the following:
You can create your Hadoop “input” directory and mount it to be HDFS then execute the following:
$ hadoop dfs -ls input
$ hadoop dfs -cat input/file01
$ hadoop jar WordCount.jar com.etapix.wordcount.App input output
This is assuming that you are running from your project home directory and that you have installed Hadoop using the Debian distribution or you can follow the rest of the tutorial from the Apache website.
Big Data is not a product – the idiosyncratic hype
Marketers have been building the hype around “Big Data”. Recruitment agencies have received many positions to fill around “Big Data”. But who is to blame when you receive a call from a recruiter who believes that “Big Data” is a product?
Recruiters
“Where ignorance is bliss, ’tis folly to be wise.” – Thomas Grays
A recruiter who does not carry out any sort of research on the role that he’s try to fill is simply an idiot. The fact is that job specs from companies can be very brief and the recruiter job is to source the best candidate for the role. Engineers hate recruiters but they feel that they need to speak to them if they have to land that next big opportunity. I had a call from a recruiter asking to send him one of our developers. He was adamant that “Big Data” was a product. He kept on asking if our developers had experience with the product called “Big Data”. I tried to make him understand that there’s no such product that I know
of. After a while, he became aggressive and I had to end the call. I blame the recruiter for lack of research and its neglect about “Big Data” and its relevant technologies. If he did carry out some research about the topic, I believe that he could have appeared more professional.
of. After a while, he became aggressive and I had to end the call. I blame the recruiter for lack of research and its neglect about “Big Data” and its relevant technologies. If he did carry out some research about the topic, I believe that he could have appeared more professional.
Engineers
IT Departments looking for “Big Data” practitioners have to share some of the blame. They write the job specs to be sent out to a recruitment company. You can’t send a job description for a data analyst and only have Hadoop and “Big Data” in it. You need to provide more information such as: job description and project background and then what sort of person you sought after. Engineers are not marketers thus they have better understanding of the technologies. We know what we want and who can do the job, therefore why not facilitate the recruiter job by providing more information beforehand?
In conclusion, Big Data is not a product and next someone calls and tells me, I will put the phone down on him. Research your topic before contacting anybody about a job opportunity.
Big Data, Bigger Myths
Working at a company which focuses in finding value in data, I often come across clients asking me about the following:
1. How “BIG” should my data be in order to considered “Big Data”
Not every company has a petabyte worth of data stored, so does size really matter? The simple answer is no. Companies should not think about “Big Data” in term of sizes but as a new paradigm. Data is stored across multiple departments which do not know how to share it and therefore impacting data-driven decision. Big Data is about accounting all enterprise data to help make timely data-driven decisions. As it was once said, all information can be accessed through few mouse clicks.
2. SQL based systems can’t do “Big Data”
This myth was created by inexperienced data “scientists” who were trying to sell their offerings. I was once in a meeting back in 2007 with a large client; they run casinos, betting sites, bingos and etc.., when a member of their tech team asked us; why can you not just run a set of SQL queries instead of exporting to an external application (built on top of Hadoop)? What I did was to take his question offline and run a demo for him. Here is what the requirement was: calculate the distance from all our members (>20m+) to each other, tell us what clubs casinos bingo are they member of, what is the closest clubs casinos bingo to them, how often do they visit the establishments and etc… Now just the distance calculation alone blew the
Oracle server away. Don’t get me wrong, SQL based systems are part of “Big Data” as they are the best way to store, retrieve the data. For simple analytics, SQL provides great tools and they are usually more mature than their NoSQL counterparts.
Oracle server away. Don’t get me wrong, SQL based systems are part of “Big Data” as they are the best way to store, retrieve the data. For simple analytics, SQL provides great tools and they are usually more mature than their NoSQL counterparts.
3. NoSQL is the way forward and Hadoop is the Holy Grail
This is a funny one. The NoSQL started as death to traditional RDBMS. Startups companies started to jump on the buzz wagon. There were NoSQL evangelist at every street corner, ok maybe not but you
get the point. And the early adopters started to see problems in the movement. Experienced data admins from the SQL world started converting then they stopped, why? To run an enterprise system, you need reliable mature application with a wealth of talents and knowledge to support it. In the SQL world, you had books and courses available for over 30 years+. It was easy to attract new talents for new projects and not to mention the tools which made life easier. Let’s get it clear, NoSQL systems are mainly used for data storage just as their SQL counter parts. Many techniques developed through decades of research around
fault tolerance, data replication and security have passed maturity and let’s not forget the compliance of industry standard. Look at it this way, Twitter still use MySQL. Hadoop is a distributed data processing framework which can also be achieve with Grid Computing, Peer 2 Peer systems and others.
get the point. And the early adopters started to see problems in the movement. Experienced data admins from the SQL world started converting then they stopped, why? To run an enterprise system, you need reliable mature application with a wealth of talents and knowledge to support it. In the SQL world, you had books and courses available for over 30 years+. It was easy to attract new talents for new projects and not to mention the tools which made life easier. Let’s get it clear, NoSQL systems are mainly used for data storage just as their SQL counter parts. Many techniques developed through decades of research around
fault tolerance, data replication and security have passed maturity and let’s not forget the compliance of industry standard. Look at it this way, Twitter still use MySQL. Hadoop is a distributed data processing framework which can also be achieve with Grid Computing, Peer 2 Peer systems and others.
4. Data scientists are to Big Data What DB admin are to RDBMS
What is a data scientist and how is it different to DB admin? No difference at all. They both do exactly the same job, trying to get value out of data. This is another of those buzzwords that publishers use to sell books and increase pages views on the net. I worked with great DB admins that new the data structure and understood it uses. We could ask the DB admin about any KPIs and he would retrieve if it was possible. If you need to write Java or any form of codes, then I’m sorry you no longer a data scientist but a programmer.
5. Big Data is a silver bullet
First of all there is no such thing as a silver bullet and big data is not an application to be implemented. Big Data is mind-set to data: capture, storing and processing. We should not think in terms of; this data is owned by X department. The data need to be integrated to give us a single view of the company. The finance department can effectively assess revenues based on marketing campaign and marketing campaign can better understand the customer based on information from the customer support team. The possibilities are endless. There is no silver bullet but we can come very close to it if we change our mindset.
Feel free to share your views in the comments section.
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