We all have seen a tremendous rise in the data generated through social media, power grid, stock exchange, retail, banking etc. in the past few years. A lot of organizations have started working on big data to increase productivity and consequently generate large revenues.
Now what becomes important are the computational skills needed to handle all the large amount of data i.e, big data that may be structured, semi-structured and unstructured data.
Indeed, there is a great need of highly skilled professionals who can perform all the tasks given to them effectively without taking too much time.
For this, they have to be excellent in handling big data issues that include processing, analyzing and storing large data sets across computer clusters, managing workloads using big data tools such as Apache Hadoop, Pig, HBase, Hive, Mahout, Sqoop, Flume, Spark, ZooKeeper etc.
Skills Required For Big Data Hadoop
To handle big data, a professional must possess the following skills.
(1) Apache Hadoop-
A professional must know how to install, configure Hadoop along with the internal working of Hadoop core components such as HDFS(Hadoop Distributed File System), MapReduce, YARN(Yet Another Resource Negotiator).
(2) Hadoop-related projects-
Knowing how to use Hadoop framework and its core components will not work. You have to be good at some of its other components which are given below.
- Apache Hive
- Apache Pig
- Apache HBase
- Apache Mahout
- Apache ZooKeeper
- Apache Spark
- Apache Sqoop
- Apache Oozie
(3) Statistical Analysis-
It will be beneficial for a professional if he has a good understanding of data statistical tools such as R programming language, SAS(Statistical Analysis System), MATLAB, Stata etc.
(4) Programming Language-
To handle big data using Hadoop, a programmer must have a good knowledge of general purpose programming languages such as Java, Python, Scala, C etc.especially for Hadoop developers whose core area is to develop Hadoop applications using these programming languages.
R is another programming language that can be used for statistical analysis and apply machine learning algorithms.
(5) SQL-
Database language SQL is required to effectively work with big data Hadoop related projects such as Hive in order to interact with the database that includes reading, writing and storing the data in the database.
(6) Linux-
If you attend any interview regarding a profile in big data Hadoop, you will be asked whether you know Linux operating system or not as most of the big data companies uses Linux to handle large data sets across computer clusters.
(7) NoSQL-
NoSQL databases such as HBase, MongoDB, CouchBase have become important tools to interact with the database while dealing with large data sets. A professional should be proficient with at least one of these NoSQL databases.
Big Data Hadoop Skills By Profession
(1) Hadoop Developer-
A big data Hadoop developer must have excellent programming skills especially in programming languages like Java and Python. Along with this he should know how to use Hive framework for querying using Hive Query Language(HQL)
(2) Hadoop Administrator-
Skills required to become a Hadoop administrator are listed below.
- Cloudera Manager Enterprise, Ganglia, Nagios etc.to add or remove nodes.
- Software installation and configuration
- Apache Hive
- Apache Pig
- MapReduce
- Linux Operating System
- Kerberos set up, etc
(3) Hadoop Tester-
Skills required to become a Hadoop tester are listed below.
- Fixing, reporting bugs
- Enhance performance of Hadoop application
- Apache Hive
- Apache Pig
- Programming languages like Java, Python
- Selenium Automation Tool
(4) Hadoop Architect-
Skills required to become a Hadoop architect are listed below.
- Should be able to make strategies after proper planning and designing to help the organization grow.
- MapReduce
- Apache Hive
- Apache HBase
- Apache Pig
- Knowledge of big data Hadoop system architecture
(5) Data Scientist-
Skills required to become a Data Scientist are listed below.
- Knowledge of how to utilize a large amount of raw data to generate some profitable business outcomes.
- Knowledge of some statistical tools such as SAS, MATLAB, Stata etc.
- Apache Hive
- Apache Pig
- Programming languages like Java, Python and especially R to apply machine learning algorithms.