Before we get into the context where Hadoop comes and fits in, firstly we need to understand what is ‘Big Data’. Big Data is no different from the normal data that we handle in our daily lives, like data we have at our Work, data on Gmail or on Yahoo mail in our inboxes (but with a small twist, it is the whole data that the Organization holds or whole of the data that Google hosts on their Gmail servers or Yahoo hosts on their Yahoo Servers and the like).
When we have such data at hand and we need to deduce the needed details out of it, is it not going to be like searching for a needle in the haystack? Definitely YES, so the answer to this question is the frameworks like Hadoop. The next few sections of this article are completely dedicated to understanding what this framework can do as a whole. Since we have discussed and introduced what data constitutes the ‘Big Data’, let us understand the characteristics of the same.
On a broader way, Big Data can be classified into 3 categories as described below:
Structured data can be classified as that category of data which can be stored, accessed and processed in a fixed format. The most convenient data that we can classify as Structured Data is the data available in our RDBMS systems (databases and tables).
Unstructured data can be classified as that category of data which has no known form or structure, being heavy in size is also another trait to this classification. Since the data that is available in this format is not structured at all, storage, access and processing of such data is also a tough process. The most convenient data that we can classify as unstructured data is the data available as the Search results from Google.
Semi-structured data is a combination of the data in the forms described above and the most convenient data that we can classify as semi-structured data is the data available in XML files.
Hadoop is a Java-based framework for programming which is basically for storage and for processing of extremely large datasets in distributed computing environments. In addition to this, it is also an open source framework and is part of the Apache project (internally sponsored by the Apache Software Foundation).
That being said, what were the traditional methodologies used earlier to Hadoop and what gave Hadoop an instantaneous success in the realm of Big Data and Analytics? The way Hadoop handles data is almost the same as any other file system and is named HDFS (Hadoop Distributed File System). Traditionally if you were to query your RDMBS servers to gain information to deduce the needed details, you would have to have a dedicated server which let's go all the Normalization details to satisfy the reporting requirements.
HDFS is more like a container for Hadoop where it stores all the data that you want to analyze further to deduce valuable information out of it. The process of processing data is done by a Java-based system known as the MapReduce. SQL or NoSQL, Hadoop is not exactly a database. Hadoop is more like a data warehouse system which needs a technique as like MapReduce to actually process the data that is held on the HDFS system.
Now with the necessary background been understood, now let us focus on the most important note on the topic – What is required of an individual to be successful with Hadoop? Let us break it down into multiple jotted points to understand the importance of each.
1. Java: Since Hadoop is basically written in Java, you need to at least have the basics of this programming language to get your hands dirty with. There is no need to be disappointed if you are from a totally different background, there are still opportunities to work on Hadoop as there are growing opportunities in the field of Big Data
2. Linux: Hadoop most basically is run on Linux for yielding better performances over Windows, so considering that basic knowledge on Linux will suffice and more the merrier.
3. Understanding on Big Data: Though this is not a definite requirement to learn Hadoop framework, an individual has to definitely understand where he/she is stepping into.
Once you have a good understanding of the above 3 points, then you can proceed with your quest to excel in Hadoop. Though Java as a technology won’t stop you from making your mark in the Hadoop framework (if you hail from a different technical background altogether, but makes the path a bit difficult as there would be a bigger learning curve).
Apache Hadoop, one of the most widely adopted enterprise solution by most of the IT giants has made it one of the top 10 IT job trends for the years 2016 and 2017 in consecution. It becomes mandatory for the intelligent professionals who want to become pros in Hadoop to learn quickly the ever evolving and ever changing ecosystem of Hadoop on a daily basis. Apache Hadoop is an open source platform built on two technologies – Linux Operating System and Java Programming language. To answer the question “Is Java knowledge mandatory to learn Hadoop?” then the answer would be a blatantly YES, as every Hadoop professional need to understand the inner workings of Hadoop which is nothing but just Java.
The Map functions that are mainly filters and sorts the data whereas the Reduce jobs which deal with the integration of the final outcomes of the map() function. The MapReduce programs can be written in various other languages like Perl, Ruby, C or Python that support streaming through Hadoop streaming API. However, there are certain advanced features that are as of now only available as Java APIs thus making it more rigid that the professionals with Hadoop have thorough Java knowledge.
If you are not from Java background, how difficult is it to learn Hadoop?
For professionals who hail from various other backgrounds like PHP, .NET, Mainframes, Data Warehousing, DBAs, Data Analytics and have a desire to get a career started with Hadoop and Big Data, this should be one of the questions that you would be questioning yourself with. If not Java, how difficult is it to learn Hadoop along with it? Let me tell you something here, you would be investing money and time and also your relentless efforts to understand and excel Hadoop but it would be better to take that additional time in understanding how Hadoop has become such a great success. It is because of Java, and if you are good enough with the basics of it just so that you understand the internals of Hadoop, you would be able to appreciate you learning towards Hadoop as well.
It is not just about Java if you look at professionals who have spent their time and money in excelling in Hadoop but with no other programming language – it gets tough for them to get themselves recruited in the fields of Big Data and Data Analytics. Most of the Organization always insist on hiring only experienced professionals who have experience with Big Data, Hadoop and any other programming languages like Java and the like. It is a fact that one needs to digest before he or she spends the time in understanding Hadoop, that Hadoop is not an easy technology to master and that too quickly. It is a sequential process where you need to spend enough time with each of the sequential steps that you take towards the learning of Hadoop.
Learning Hadoop as we just discussed is not going to be an easy task, but if professionals know about the hurdles with it then overpowering those hurdles and making the learning hassle-free is very much possible. Since Hadoop is an open source framework that was built on top of Java, hence making it necessary for all the professionals to be well-versed with the basics of Java essentials to appreciate Hadoop and also its internals. Having a good knowledge of the advanced Java concepts for Hadoop is a great advantage but definitely not compulsory if you are not from the Java background. Hope the answer to this infamous question is thought to be obtained here, with the necessary steps that one needs to take to surpass the hurdle if you are a non-Java programmer.
In this article, we have seen what actually is Big Data and understanding in what context does Hadoop exactly fit in. With that understanding, we have also introduced the basics of Hadoop and how it works.
Coming on to the most important point of the article, if you are a Java developer then your learning curve to excel in Hadoop is small and if you hail from a different technical background then, your learning curve is a little bigger (as you need to understand and appreciate the inner workings of Hadoop).
|Big Data On AWS||Informatica Big Data Integration|
|Bigdata Greenplum DBA||Informatica Big Data Edition|
|Hadoop Testing||Apache Mahout|
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.