What Are the Skills Needed to Learn Hadoop?

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Big Data is a term that refers to large, unstructured, and complex data sets that cannot be processed by conventional data processing software. Such software falls short when it comes to capturing, analyzing, curation, sharing, visualization, security, and storage of large amounts of data. Due to the unstructured nature of big data, any attempt to integrate it using traditional software results in errors and clumsy operations. As such, big data platforms strive to process data more efficiently while minimizing error margins, in comparison to how relational databases are used for routine data processing.

By 2025, over 150 zettabytes (150 trillion gigabytes) of data will need to be processed and analyzed, according to a 2019 Forbes article on big data. Additionally, 40% of businesses surveyed indicated that they frequently need to manage unstructured data. This demand will exacerbate data management issues, necessitating the use of frameworks such as Hadoop. These tools simplify data analysis and processing.

What Is Hadoop?

Apache Hadoop is a framework for processing large data sets in a distributed fashion across clusters of computers using simple programming models. Hadoop is composed of the following four modules:

  1. Hadoop Common
    A collection of utilities and libraries that provide support for other Hadoop modules.
  2. Hadoop Distributed File System (HDFS™)
    A distributed, fault-tolerant, self-replicating file system that clusters data and enables rapid access to it.
  3. Hadoop YARN
    A processing layer that manages resources, schedules jobs, and handles a variety of processing needs in aggregate.
  4. Hadoop MapReduce
    MapReduce is referred to by IBM as the “heart of Hadoop.” It is a batch-oriented rigid programming model that enables large datasets to be processed across a cluster of nodes/machines. It divides data processing into two stages: mapping and reduction. By utilizing the Mapper function, the Mapping phase is responsible for working with small chunks of data distributed across the cluster. The Reducing phase aggregates the data using a reducer function.

Benefits of Using Hadoop 

The importance of timely data processing cannot be overstated in business. While there are numerous frameworks available to accomplish this, businesses that adopt Hadoop do so for the following reasons:

  • Scalability
    Businesses can process and derive value from petabytes of HDFS data.
  • Flexibility

    Access to a variety of data sources and data types is simple.
  • Speed
    Parallel processing and minimal data movement enables rapid processing of large amounts of data.
  • Adaptability
    It is compatible with a number of programming languages, including Python, Java, and C++.

A practical illustration of this is provided in the following use case:

Business Use Case: SEARS

By 2013, Hadoop had been adopted by more than half of the Fortune 500, including Sears. The company desired to gain reliable insight by analyzing customer sentiment data, churn data, and POS transactions. According to Phil Shelley, then-VP and CTO of Sears, implementing Hadoop was a move to “…personalize marketing campaigns, coupons, and offers down to the individual customer.”

However, the systems Sears was using at the time were incapable of achieving these objectives due to ETL limitations and storage grid shortcomings. The company received a large amount of data but could only access about 10% of it in order to generate reports. The remainder was too much for the storage grid to handle.

Sears was able to analyze ALL of the data streaming in and begin gaining useful insights as a result of the implementation of Hadoop.

This is one example of a business that has revolutionized its data management practices as a result of Hadoop. The framework establishes the minimum requirements for data storage, processing, and analysis. Additionally, it is less expensive in comparison, as it can run on low-cost hardware or in the cloud.

What Is a Hadoop Developer?

A Hadoop developer is a software developer who specializes in Big Data and, more specifically, the Hadoop ecosystem. Though responsibilities vary according to years of experience, the following are the responsibilities of a Hadoop developer:

  • Programming in accordance with system designs and creating solutions/applications/APIs to address business use cases
  • Defining work processes
  • Developing solutions for reviewing, mining, and analyzing logs or data
  • Utilizing cluster services within the context of the Hadoop ecosystem
  • Acquire a thorough understanding of Hadoop Common and the Hadoop ecosystem in general.

To become a Hadoop developer, you’ll need the following skills:

  • From a programming standpoint, problem solving
  • Designing and architecting
  • Documenting the design, scheduling, and use of workflows
  • Data loading and all other aspects of data manipulation in a variety of formats

Skills Needed to Learn Hadoop 

Anybody can learn Big Data technologies and frameworks such as Hadoop if they are committed and believe they will benefit some aspect of their work or career growth. While no prerequisites are required for learning Hadoop, having a firm grasp on the following areas will aid in comprehension:

Programming Skills

Hadoop requires familiarity with a variety of programming languages, depending on the function you want it to perform. R or Python, for example, are useful for analysis, whereas Java is more useful for development work. However, it is not uncommon to find beginners with no prior knowledge of information technology or programming learning Hadoop from scratch.

SQL Knowledge

SQL proficiency is required regardless of the role you wish to pursue in Big Data. This is because many organizations that previously relied on relational databases are now entering the Big Data space or integrating their existing infrastructure with a Big Data platform. While many existing data sets are structured, even unstructured data can be structured to meet processing requirements. Additionally, Big Data platforms built on the Hadoop ecosystem include packages such as Hive or Impala, as well as Spark components such as Spark SQL, which all require knowledge of SQL or SQL-like querying languages. One can benefit from prior experience with or knowledge of SQL and can easily process large datasets using newer tools and technologies without worrying about underlying processing frameworks.

Linux

Because the majority of Hadoop deployments across industries are Linux-based, it’s advantageous to have some prior working knowledge of Linux. Additionally, Hadoop versions 2.2 and later include native Windows support.

Career Benefits of Doing Big Data and Hadoop Certification

As long as you work in a data-intensive company, a Big Data Hadoop certification will help you advance your career. Several career advantages include the following:

1. Hadoop and Big Data Are Relevant for a Diverse Range of Professionals

The Hadoop ecosystem is made up of tools and infrastructure that can be used by professionals with a variety of different backgrounds. Big data analytics’ growth continues to create opportunities for professionals with an IT or data analysis background.

This expansion benefits the following professions:

  • Software developers
  • Software architects
  • Data warehousing professionals
  • Business analysts
  • Database administrators
  • Hadoop engineers
  • Hadoop tester 

For example: 

  • As a programmer, you can create MapReduce code and script it using Apache pig.
  • You can use Hive as an analyst or data scientist to perform SQL queries on data.

2. Hadoop Is on a High Growth Path

The Big Data landscape has evolved over time, and a significant number of large companies have adopted Hadoop for big data analytics. This is because the Hadoop ecosystem encompasses a number of technologies critical to implementing a successful Big Data strategy.

According to the most recent data from Google Trends, Hadoop and Big Data have grown at the same rate over the last few years. This indicates that Hadoop will retain its importance as a tool for enabling better data-driven decisions for the foreseeable future. As such, in order to become indispensable to any company (and thus qualify for elite roles such as Data Scientist, Big Data Engineer, etc. ), it is critical to learn and develop proficiency in all of the Hadoop technologies.

Figure 1: Hadoop and big data both grow at a similar rate on average.

3. High Demand, Better Pay 

As previously stated, Hadoop is a low-cost, fast, scalable, and adaptable data storage system. The Hadoop ecosystem, together with its suite of technologies and packages such as Hive, Spark, Kafka, and Pig, successfully supports a diverse range of use cases across industries, enhancing Hadoop’s prominence.

According to IBM and Burning Glass technologies, Apache Hadoop, MapReduce, Apache Hive, and Pig are among the most in-demand and well-compensated data science skills. Possessing these skills will significantly increase your earning potential, allowing you to earn well over $100,000.

Figure 2: Hadoop is one of the most highly compensated analytical skills.

Additionally, the same report identifies Apache Hadoop as a “disruptor,” indicating that jobs classified in this quadrant “…. Present the greatest concern; in the future, they are the jobs most likely to face supply shortages, preventing firms from fully utilizing Big Data.” Additionally, these skills take a long time to hire, which could be attributed to the following:

  • Inadequate relevant skills or an inability to find the optimal combination of skills
Figure 3: Jobs requiring Apache Hadoop expertise will become increasingly difficult to fill in the future.

While having Big Data and Hadoop skills is critical, the hiring process is more nuanced for employers, depending on the industry and role to be filled. Typically, it takes time to develop the optimal combination of domain knowledge and data analytics skills.

Bottom Line: Your Learning Journey Continues 

Given the foregoing, it’s clear that learning Big Data and Hadoop holds real promise. As long as we continue to generate data from every aspect of our digital lives, Big Data processing software such as Hadoop will have a place.

While a certificate is an excellent place to start your Hadoop career, you will need to put in some time and effort to become an expert. You can accomplish this by following the steps below:

1. Keep Practicing 

A comprehensive Hadoop course will provide you with ample opportunity for hands-on practice. Allow your knowledge to remain dormant while you seek employment. After your course, create a virtual machine and practice with additional data sets.

2. Follow Some Established Professionals or Companies

You will benefit in the following ways by following industry professionals:

  • Keeping abreast of current trends in your industry
  • Obtaining assistance with troubleshooting
  • Acquainting yourself with the latest releases and their implications for your career

3. Pursue an Advanced Course

Once you’ve mastered Hadoop, enroll in some advanced courses that will catapult you into more lucrative career opportunities. It’s always prudent to begin with a career objective, a certification path toward that objective, and key milestones that will aid in tracking your progress and keeping you focused on your objective. A Big Data and Hadoop course validates your technical proficiency with Big Data tools and assesses your familiarity with Hadoop. You gain practical experience working on real-world projects, develop problem-solving abilities with Hadoop, and gain an advantage over other job applicants.