The Comprehensive Starter Kit Before You Learn Big Data

What Is Big Data?

We generate data at every point and millisecond of the day. Whether it is an online digital activity or offline shopping history at a neighborhood grocery store, we are creating data. This data is growing at an exponential rate. The data is analyzed for insights that power decisions and strategize business moves. It is this power of Big Data which brings about change in the world and has made it the ‘new oil.’ 

The importance of data does not come from how much data you have; but what you do with it. This is where the Big Data expert comes in. Big Data professionals make use of technology platforms and tools to gather, clean, and analyze the data; and bring about change in business processes and our everyday lives.

The best definition of Big Data can be found in the McKinsey report of 2011, “Big Data: The Next Frontier for Innovation, Competition, and Productivity.” 

“‘Big Data’ refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.” 

Thus, Big Data course refers to the large and complex datasets streaming in real-time and at high speeds, as in social media or meteorology. Big Data is a term coined to explain data that has characteristics such as Volume, Velocity, Variety, Viscosity, Veracity, Validity, Volatility, and Virality. This data comes from disparate sources in multiple formats. The available and traditional database management tools or data processing applications are not capable of capturing, storing, curing, mining, managing, securing, processing, analyzing, and crunching the massive data efficiently. So dedicated technologies and tools have evolved, and IT professionals are required to master these.

Types of Data

The 3 Types of Big Data

To gain an understanding of Big Data, it is necessary to know the different types of Big Data and the features that characterize Big Data in the context of Variety.

Data is classified based on a pattern or schema that is present within the data. 

• Structured Data

  It refers to the type of data that has a clear evident pattern or schema that can be predefined before storing the data and is stored or processed in a fixed format. A table that stores the marks of students in a class is an example of structured data.

• Semi-structured Data
  

  Data that has incomplete pattern or schema and which has no fixed or structured format, with different semantic elements and combinations of arrangement. Location data stored in the XML file is an example of semi-structured data.

• Unstructured Data
  

  Unstructured data denotes the absence of pattern or schema and is of unknown or mixed formats and cannot be processed unless organized or usage of tools/utilities capable of handling such data. Most data nowadays fall into this category. Text files, social media content, and images are examples of unstructured data.

Characteristics of Big Data

Big Data has specific characteristics. The volume of data states its massiveness. The velocity or speed indicates the rate at which the data is generated and processed, which is very high. The heterogeneous nature or variety of data states the different formats of data that is being generated and is huge, like emails, images, audio, and so on.  Variety also constitutes the characteristic that data is variable and changing every second. The veracity of the data relates to the quality of data, which is usually unknown, as data may be incomplete, corrupt, or from untrusted sources.

Why You Should Learn Big Data 

The growth of Big Data market size forecast (2011-2027) - Statista

According to the “Big Data Market Size Worldwide Revenue Forecast (2011 to 2027)” by Statista, the Global Big Data market is expected to reach $103 billion by 2027, growing at a CAGR of 10.48% during the forecast period 2011-2027. With phenomenal advances in technology, a rise in the use of smartphones, and a 24/7 connectivity, more and more data is created as every activity is getting digitized. Organizations are identifying opportunities to gather, analyze, and create a competitive edge. They are tapping new ways for cost reduction, business efficiencies, and customer satisfaction. The use of Big Data technologies like Hadoop has become the go-to practice for keeping up with Big Data challenges. Big Data is present in every industry – healthcare, sports, finance, insurance, eCommerce, eGovernance, natural resource management, IT, manufacturing, politics – it offers you a range of career choices across domains. 

Big Data adoption and importance by Industry – Dresner Advisory Services

If you want to work at companies working with Big Data, you need to learn Big Data technologies and sharpen your skills. The more skilled you are at Big Data technologies like Hadoop, the more likely you are to command better career opportunities. 

With Big Data and analytics becoming mainstream, all firms are investing in Big Data technologies, creating unlimited opportunities. At most places, Apache Hadoop is the commonly used Big Data technology. Hadoop, as a framework and Hadoop ecosystem, along with its products, has evolved, widely accepted, and implemented across industries. The broader acceptance of this framework for all data needs and the fact that organizations can no longer avoid investing in data stating “uneconomical” makes a compelling reason to begin mastering Big Data with Hadoop.

Prerequisites Before Starting to Learn Big Data  

Big Data does not conform to a single technology but a cluster of several technologies and tools. So the first step to mastering Big Data is to hone some of the necessary skills that form the core of your Big Data learning. The next step would be to get yourself certified in a popular Big Data program that gives you an overview of leading technologies and tools in use. 

Some of the essential prerequisites for learning Big Data are:

1. Familiarity with Linux 

As Hadoop is usually installed on the Linux OS, a basic working knowledge of the Linux OS is a prerequisite for working on a Big Data platform like Hadoop.

2. Sound knowledge of a query language like SQL

Even without a hardcore programming background, a good understanding of SQL helps you to master Hadoop through hands-on training. As Hadoop uses many software packages that extract data using SQL like queries, SQL/ MySQL knowledge is a prerequisite for learning Big Data.

3. Working experience with some of the tools used to manage Big Data, or built on top of Hadoop

Hive(data warehouse package), Pig(scripting), and HBase(NoSQL DB) are some of the packages/services used in the storage, extraction, transformation, processing of data. Hands-on experience of one or more of these software packages is necessary for working with Big Data.

4. Good understanding of the Hadoop architecture, storage, and distributed computing 

This is the ultimate key to helping you model better solutions using Hadoop.

5. Knowledge of Java 

Java not only helps you to write your basic programs but also gives you an added advantage as you can make use of the advanced features available only in the Java API. 

Ultimately, these prerequisites make up your Big Data arsenal. So make sure that you have these essential prerequisites before you start on your Big Data learning curve.

Some Must-have Skills are:

1. Programming
2. Data Warehousing
3. Computational frameworks, architecture, and networks
4. Statistical analysis
5. Machine learning and data mining
6. Data visualization
7. Problem solving and creativity
8. Business knowledge of the field in which the Big Data is applied

Mastering these Big Data skills make you eligible for the many Big Data job roles available in the market.

Why Learn Hadoop?

The Big Data market is evolving at a faster pace, with Hadoop leading the way. It is not only a groundbreaking solution for Big Data problems but also opens up the road to many other Big Data technologies. Regardless of technical background or programming knowledge, any of you can start a Big Data career with the necessary skills and understanding of the Hadoop ecosystem. As Hadoop has multiple tools, it can be mastered easily, even by those with different programming backgrounds. For instance, a software programmer can begin with MapReduce jobs in Java, or Python. One who is more SQL savvy can work with Apache Hive or Apache Drill.

The demand for Hadoop professionals is rising worldwide. As of November 2018, 45 percent of professionals in the market research industry used Big Data analytics as a research method (Statista). This translates into countless job opportunities for IT professionals like software developers, software architects, database professionals, mainframe professionals, project leaders, or other graduates looking for a career in Big Data. As the demand far exceeds the availability, companies are willing to pay attractive packages for skilled developers. So start your career in Big Data with Hadoop, to make the most of the job market.

Job Roles in Hadoop

As Big Data is revolutionizing industries and applications, Hadoop is emerging as a game-changer across scenarios. Getting hold of the right job role in the Big Data landscape can be the last stepping stone in your career, as salaries associated with these profiles are quite high. 

Below you can find the details regarding the job roles available in different sectors along with the responsibilities associated and who can apply for such positions.

Infrastructure 

Positions:

• Big Data engineer
• Platform engineer
• Infra specialist/engineer
• Consultant
• SME
• Architect

Responsibilities:

• Install and setup clusters
• Components/tools within a cluster
• Deployment of services 
• Managing, monitoring, maintaining, upgrading, scaling your platform
• Integration with different non-Big Data tools and technologies
• Architecting, designing, capacity planning, etc.

Requirements:

• Admin/scripting/support background (servers, dba, systems)
• SME with systems and tools, network, hardware 
• Basic coding knowledge in Java, C++, Python, etc.
• People with virtual machines/cloud-based environment experience

Development

Positions: 

• Hadoop/spark/python/scala developer
• j/m/s consultants
• Big Data engineer with programming skills
• Data analyst
• Data mgmt consultant

Responsibilities:

• Developing solutions/APIS/frameworks/Applications

Requirements:

• Knowledge of programming in one or more languages
• Virtual machines/cloud-based environment experience
• Experience with git/bitbucket, other agile methodologies, and tools
• Experience with SW testing
• Working with data in different formats/volumes 

Data Science

Positions:

• Data Scientists
• Consultants
• Senior developers

Responsibilities:

• Work with data, statistical models, mathematical models
• Create your model---analyze data, visualize data, extract useful insights from data, identify patterns
• Work with analytics related to tasks-predictive/preemptive 

Requirements:

• Knowledge of statistics, mathematics
• Analytical thinking
• Good knowledge of different tools/languages/components which allow working on data
• Coding knowledge
• Experience with git/bitbucket, other agile methodologies, and tools.

Who Is a Hadoop Developer? 

The Hadoop Developer is one of the most sought after job roles. He is much like the programmer who is proficient in the Big Data domain. A Hadoop Developer is also a project consultant who leads a team of developers into building applications and Big Data solutions. 

Some of the vital must-have skills are:

• Strong design and architecture skills
• Excellent documentation skills 
• Experience of data loading tools, schedulers, and computation frameworks
• Knowledge of Java and JVM-based language, MapReduce, HiveQL 
• Familiarity with HBase, Kafka, ZooKeeper, and other Apache software 

Are you skilled enough for a Big Data career? Try answering these Big Data and Hadoop Developer Test Questions and find out now!

Career Benefits of a Big Data and Hadoop Course 

Big Data certification is a validation of Big Data expertise and proficiency in using Hadoop technology. A Big Data and Hadoop course endorse your technical skills working with Big Data tools and measures your knowledge of Hadoop. You gain hands-on experience working on live projects, learn problem-solving skills using Hadoop, and gain an edge over other job applicants. 

With an explosion of Big Data and Hadoop job availabilities, this is just the right time to start your career in Hadoop and enjoy the current opportunities in the Big Data market.