What are the prerequisites to learn Hadoop?

Today, Big data and Hadoop are synonymous. Not to mention, Hadoop has proved itself as a revolutionary tool for Big data analysis.  With its enormous popularity in the market, almost every professional want to learn Hadoop and shift in the Big data domain. But how much complex it is?

Hadoop is a single complete product that is commonly known as Hadoop eco-system and consists of many open source products like HDFS, MapReduce, Hive, Pig, Ambari, Flume, Mahout, etc. In addition to that, the entire Hadoop system runs on Linux-based operating system. Furthermore, with the day by day advancement in this area more and more open source tools are being added. As a result, Hadoop learning space is becoming broader day by day.

Prerequisites for learning Hadoop

As Hadoop is a complex infrastructure, learning Hadoop needs some prerequisites based on its different roles and operations.

To work with Hadoop, the skills one must possess depend on his role and the operational areas he will deal with. So, let’s have a look at Hadoop professional roles and operational areas first.

Hadoop Professional Roles

• Hadoop Developer
• Hadoop Architect
• Hadoop administrator
• Hadoop Data scientist
• Hadoop tester

Hadoop Operational Areas

• Data storing
• Data extraction
• Data query
• Data processing
• Data analysis

Overall, the below skills are considered as prerequisites for Hadoop considering above-mentioned roles and areas.

• Programming knowledge
• Knowledge of Linux commands
• Problem-solving skill
• Knowledge of SQL
• Knowledge of statistics

Know the particular area of expertise as prerequisite

Programming knowledge: MapReduce is the main programming block of Hadoop, and it uses Java for data processing. Moreover, Hadoop is based on Java, hence knowing Java is an advantage to work with the components as close as possible.

However, the tools like Hive, Pig provide their own high-level interaction languages to process data internally with MapReduce, and you can skip complex MapReduce programs through it.

But as Hadoop is written in Java, Java is the language to go with if you want to know the nuts and bolts of Hadoop to debug complex issues.

Along with Java, knowledge of Scala and Python helps a lot to understand data analysis in Hadoop.

Knowledge of Linux commands: Though Hadoop can run on Windows it is built primarily for Linux. Hence, Linux is the preferred method to install and manage the Hadoop cluster. So working knowledge of Linux, especially Linux commands help a lot to work with Hadoop HDFS.

Knowledge of SQL: Data query and ETL are essential operations in Hadoop where SQL or SQL like syntax is used. Hence, SQL commands for joins, order, group by, etc. are widely used in Hadoop. Therefore, if you are already familiar with SQL, you can make use of existing knowledge. Otherwise, you need to learn and use SQL like syntax.

Furthermore, Apache Hive query language is similar to SQL. Besides, Apache Pig also has many commands which are similar to SQL commands. Additionally, tools like Cassandra and HBase also provide SQL like query interface to interact with data.

Problem-solving skill: This is an essential requirement for Hadoop data engineer who needs to deal with machine learning algorithms to work on complex data analysis. Agility towards mathematical problems is a must to play the role.

Knowledge of statistics: The sole purpose of Hadoop is data analysis where probability and statistical methods play a significant role if you are working as Hadoop data scientist. Hence, knowledge of statistics is a plus.

The prerequisites mentioned above are not mandatory. Though knowing them will definitely help one to understand and learn the Hadoop system faster with more workability.

Learn and go big with Big data Hadoop!