John Mashley, a computer scientist, first coined the term ‘big data’ in early 1990s. Since then, the definition of this term has changed a lot. Moreover, until late 2012, applications of big data were almost next to none. This late inception hasn’t stopped this technology from revolutionizing the tech industry, though.

Although it has been almost half a decade since the massive blow-over of big data occurred, applications of big data services are just increasing by the day. Because of this, tech companies seem to always have a good number of openings for big data professionals all the time. While this is one of the reasons to learn Hadoop this year, it is just the tip of the iceberg.

Before looking at the major reasons to upskill to Hadoop, let’s first understand what big data is and why Hadoop is essential in this domain.

The latest definition of big data proposed earlier this year states, "Big data is where parallel computing tools are needed to handle data. This represents a distinct change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd’s relational model."

Seems like a handful, right? Let’s make it simple for you.

In simple terms, big data is a collection of data sets that are so voluminous and complex that traditional data-processing systems are inadequate of working with them. Adding to this, big data has five generic characteristics. These are:

Volume:This is the quantity of data. It is defined in comparison to the system that would be used to process it to ensure whether the data qualifies to be called big data.

Variety:This defined the type and nature of the data. Big data derives from text, images, audio, video and all sorts of formats. Additionally, it completes missing pieces in the data through data fusion.

Velocity:This is basically the speed at which the data is generated and processed. Big data is often available in real-time and needs to be processed consistently.

Variability:Unlike traditional structured data, big data is not homogenous. It contains inconsistencies and a mix of different types of data sets. This makes it impossible to analyze and process the data using traditional systems.

Veracity:This is the term that defines the quality of data available. Veracity can greatly affect the processing speed and other parameters of the data. And, because big data has this characteristic, it’s not easy to process using traditional approaches.

Hadoop, at its core, makes it convenient to warehouse, process and analyze this atypical data which sets it apart from traditional data handling software.

Now that you have a basic understanding of what big data is and why Hadoop is required, let’s look at the top five reasons to get into this ever-growingtechnology this year.

Hadoop is a Disruptive Technology

Traditional data warehousing and analytics systems had several disadvantages. Hadoop overcomes these disadvantages because of added factors like scalability, storage, flexibility to use several data sources, cost and performance. Looking at it through the eyes of a data scientists, Hadoop has revolutionized how data is handled and analyzed.

Google Trends Graph Depicting Hadoop and Big Data

Moreover, the market trends are favoring Hadoop and other big datatechnologies. The graph above clearly shows that big data technologies are here to stay and will keep bringing about revolutions in data analytics and warehousing for years to come.

A Gateway to Big Data Technologies

When it comes to handling and manipulating big data, there are several tools that are used in the market right now. Despite this trend, most companies are inclined towards using the Hadoop ecosystem for most of their projects and coupling it with other software for added features. In a way, Hadoop has become the de-facto for big data technologies and anyone who is planning to make a career in big data and analytics need to start their journey via Hadoop.

No matter what domain in big data you would want to get into, it is mandatory for you to not just learn Hadoop, but also master other technologies falling under the Hadoop ecosystem.

Complete Hadoop Ecosystem

A Top Priority in Major Organizations

According to Forbes, big data adoption reached a whopping 53% in 2017. This is up from 17% in 2015, with services like telecom and finance leading early adopters. And most analysts predict that this number will be even higher this year.

The question here is, why is there such a massive growth?

It’s almost common sense really – thebigger the dataset is, the more the insights derived from it will be. As big data uses huge chunks of data which by their nature are heterogenous and involve several different formats, analyzing this data using tools like Hadoop and Spark, analysts can identify several hidden patterns. These could include unknown correlations, customer preferences, market trends etc. This allows businesses to make better and improved business decisions and strategies.

Distribution of Structured v/s Unstructured Data

The above image shows the distribution of structured and unstructured data globally. You can clearly see that unstructured data or big data is and will be clearly overshadowing structured data way into the future. As per the prediction of this trend, along with the massive advantages of big data analytics, most major companies in almost every domain are giving apex priority to this field.

Fat Paychecks

Industry trends suggest that there is a moderate to high difference between availability and demand for skilled big data professionals. Because of this, companies offer quite hefty packages to white-collar workers with the necessary skills to work in the big data handling, management and analytics streams.

As per PayScale.com, the average salary of Hadoop professionals can be anything ranging from $97,000 to $134,000 annually, based on the job role they are offered.

Adoptable by Professionals from Different Backgrounds

Big data is an ecosystem that has a variety of tools and services available. Not just this, the services here can be leveraged by professionals from various backgrounds.A few examples for this would be:

1. A programmer can work with MapReduce and use his/her honed skills in Python, Java or any other programming language.
2. A scripting professional can adopt Apache Pig which uses Pig Latin. Pig Latin is very similar to traditional scripting making it ideal for professionals comfortable with scripting.
3. A professional with proven expertise in SQL or DBA can take up a role with Apache Hive or Drill as these use SQL-like queries for processing.

But who are the ideal candidates to upskill to big data and Hadoop? You should consider getting into Hadoop and related big data technologies if you recognize yourself in any of the following categories:

• Recent Graduates looking to build a career in the Big Data Field
• Software Developers
• Software Architects
• Testing Professionals
• Project Managers
• Analytics & Business Intelligence Professionals
• ETL & Data Warehousing Professionals
• DBAs & DB Professionals
• Senior IT Professionals
• Mainframe Professionals

To top it all off, there are several different profiles in the big data field. Some of the mainstream job roles include:

• Software Engineer
• Senior Software Engineer
• Data Analyst
• Data Engineer
• Data Scientist
• Hadoop Developer
• Hadoop Admin
• Big Data Architect

Hadoop is revolutionizing how data is stored, processed and analyzed. Because its number of advantages, Hadoop is definitely a future-proof technology which will keep changing the IT industry for the better.

Now that you are aware of the major reasons why you should learn this skill, why not start now and learn Hadoop this year?