“Big Data” describes data sets that are too massive, complicated, and/or poorly organized to be handled by traditional data analysis tools. Capturing, analyzing, curating, sharing, visualizing, protecting, and storing enormous volumes of data are all areas where this kind of software falls short. Big data is notoriously difficult to integrate with conventional software due to its chaotic nature. When compared to the usage of relational databases for mundane data processing, big data platforms aim to process data more effectively while reducing error margins.
According to a 2019 Forbes article on big data, by 2025, over 150 zettabytes (150 trillion gigabytes) of data would need to be handled and evaluated. As a further finding, the poll found that 40% of firms routinely deal with the necessity to manage unstructured data. This need will increase the complexity of data management and drive the use of tools like Hadoop. Data analysis and processing are made easier by these technologies.
What Is Hadoop?
Apache Hadoop is a platform for distributed processing of big data sets across clusters of computers with the help of low-level programming paradigms. The following are the four parts that make up Hadoop:
- Standard Hadoop
- Comprised of several helpful tools and libraries, these Hadoop modules serve to supplement the functionality of the framework as a whole.
- Hadoop’s distributed file system, HDFSTM.
- A file system that is distributed, fault-tolerant, self-replicating, and uses data clusters to improve performance when accessing large amounts of data.
- Distributed File System in Hadoop Using YARN
- A layer of processing that coordinates the allocation of resources, the scheduling of jobs, and the general management of processing demands.
- MapReduce in Hadoop
- “MapReduce is at the heart of Hadoop,” IBM says. In order to process massive datasets, it employs a strict programming approach based on batch processing, which distributes tasks between a group of nodes. The procedure splits processing of data into two phases: mapping and reduction. In the Mapping phase, the Mapper function is used to collect and organize data from various nodes in the cluster into manageable pieces. In the Reducing step, a reducer function is used to compile the data.
Benefits of Using Hadoop
In the commercial world, processing data quickly is of paramount significance. Although this may be done using a number of other frameworks, many companies are adopting Hadoop for the following reasons.
- HDFS allows businesses to process and get value from petabytes of data.
- It is easy to get your hands on many different kinds of data from many different sources.
- Processing massive datasets in a short amount of time is made possible through parallel processing and minimum data transportation.
- Some of the languages it works with include Python, Java, and C++.
Here’s a use case that illustrates this in action:
Business Use Case: SEARS
More than half of the Fortune 500 had implemented Hadoop by 2013. That number included Sears. To this end, the business aimed to analyze things like customer feedback, churn rates, and POS spending in order to get useful intelligence. The goal of Sears’ use of Hadoop, as explained by Phil Shelley, former VP and CTO of the company, was to “…personalize marketing campaigns, discounts, and offers down to the individual consumer.”
But owing to ETL constraints and storage grid deficiencies, Sears’ then-current systems couldn’t help the company accomplish those goals. However, only around 10% of the data obtained by the organization was actually usable in order to make reports. The remaining quantity exceeded the capacity of the storage network.
After using Hadoop, Sears was able to process all incoming data at once and start getting insights.
Using Hadoop, one company was able to completely revamp how it handled data. The framework specifies the bare minimum needed to store, process, and analyze data. Since it may function on inexpensive hardware or in the cloud, it also has a lower total cost of ownership.
What Is a Hadoop Developer?
To put it simply, a Hadoop developer is a software engineer with expertise in Big Data and, more particularly, the Hadoop ecosystem. The following are some of the duties of a Hadoop developer, however they will change as the developer’s experience grows:
- Implementing system designs in code and developing software or application programming interfaces (APIs) to solve business problems are all examples of programming.
- Workflow definition
- Creating methods of evaluating, mining, and analyzing data logs
- Incorporating Hadoop ecosystem cluster services
- Be well-versed with Hadoop Common and the broader Hadoop ecosystem.
To work as a Hadoop developer, you need to have the following abilities:
- Programmatically speaking, problem-solving
- Planning and building
- 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
If one is dedicated and has faith that learning Big Data technologies and frameworks like Hadoop would help them in some way, they can learn them. Although there are no strict requirements, it will help to have some familiarity with the following before diving into Hadoop:
Different tasks for Hadoop call for knowledge of different programming languages. For analysis, languages like R and Python are helpful, whereas Java is preferable for development. However, novices with no background in IT or programming are frequently seen learning Hadoop from scratch.
Having SQL skills is essential in the Big Data industry. This is so because many companies that have historically depended on relational databases are either venturing into the Big Data arena or combining their existing infrastructure with a Big Data platform. Even though many databases already exist with a predetermined structure, unstructured data can be organized in order to be processed. Also, knowing SQL or a SQL-like querying language is helpful when working with Big Data systems based on the Hadoop ecosystem, which include packages like Hive or Impala and Spark components like Spark SQL. One may quickly handle massive datasets utilizing modern tools and technologies without worrying about underlying processing frameworks, and one can benefit from having expertise or knowledge of SQL.
Because the majority of Hadoop deployments across industries are Linux-based, it’s advantageous to have some prior working knowledge of Linux. Moreover, Hadoop 2.2 and subsequent versions offer native Windows compatibility.
Career Benefits of Doing Big Data and Hadoop Certification
Gaining a Big Data Hadoop certification is beneficial to your career if you work for a firm that relies heavily on data. There are a number of benefits to your career, including:
1. Hadoop and Big Data Are Relevant for a Diverse Range of Professionals
Hadoop is an ecosystem of software and hardware that may be utilized by experts in a wide range of fields. As the field of big data analytics expands, more and more job possibilities will arise for those with IT or data analysis training.
The following occupations will benefit from this growth:
- Creaters of Software
- Developers of computer software
- Information warehouse experts
- Executives who analyze businesses
- Data managers
- Specialists in Hadoop
- Tester of Hadoop
Consider the following case:
- If you’re a developer, you can use Apache pig to script your own MapReduce code.
- An analyst or data scientist can use Hive to run SQL queries on data.
2. Hadoop Is on a High Growth Path
Hadoop has been embraced by several large corporations for use in big data analytics as part of the expanding Big Data ecosystem. This is because many tools necessary for a successful Big Data strategy implementation are part of the Hadoop ecosystem.
Most current Google Trends data shows that both Hadoop and Big Data have risen at roughly the same rate in popularity over the past three years. This suggests that Hadoop will continue to play an essential role as a means of facilitating improved data-driven decision making for some time to come. To become an invaluable asset to any firm (and thus qualify for top positions like Data Scientist, Big Data Engineer, etc.), it is essential to understand and master all of the Hadoop technologies.
Figure 1: Hadoop and big data both grow at a similar rate on average.
3. High Demand, Better Pay
Hadoop is a distributed file system that can store large amounts of data quickly and easily. It is also inexpensive, scalable, and customizable. Hadoop’s significance has been increased because to the Hadoop ecosystem and its suite of technologies and packages, including Hive, Spark, Kafka, and Pig, which successfully serve a wide variety of use cases across a wide range of industries.
IBM and Burning Glass technologies found that expertise in Apache Hadoop, MapReduce, Apache Hive, and Pig are some of the most sought after and lucrative data science specializations. To put it another way, if you possess these abilities, you may expect to make well over $100,000 each year.
Figure 2: Hadoop is one of the most highly compensated analytical skills.
Positions in this quadrant “…. Present the biggest concern; in the future, these are the jobs most likely to encounter supply shortages, preventing organizations from properly exploiting Big Data,” the research said, further labeling Apache Hadoop as a “disruptor.” Moreover, it takes a long time to employ people with certain talents, which may be due to the following:
- Not having enough of the right talents or the right mix of skills
Figure 3: Jobs requiring Apache Hadoop expertise will become increasingly difficult to fill in the future.
Skills in Big Data and Hadoop are in high demand, but companies still face a varied hiring process based on the sector and the nature of the position being filled. It usually takes some time to get the best possible proficiency in both subject expertise and data analytics.
Bottom Line: Your Learning Journey Continues
All of this points to the value of studying Big Data and Hadoop. Big Data processing technologies like Hadoop will remain relevant so long as we continue to produce data from every part of our digital lives.
While becoming certified is a great first step, becoming an expert in Hadoop still requires significant time and work. Here are the measures you can take to achieve this:
1. Keep Practicing
A thorough Hadoop training will give you with sufficient chance for hands-on experience. Put your skills on hold while you look for work. If you want to continue learning after your course is over, you may do so by setting up a virtual computer and working with extra data sets.
2. Follow Some Established Professionals or Companies
By taking their lead, you may expect to reap the following benefits in your own work:
- Knowledge of the most recent developments in one’s field
- In search of aid in fixing an issue
- Keeping up with current events and their potential impact on your job.
3. Pursue an Advanced Course
When you’ve gotten the hang of Hadoop, it’s time to take some additional classes that can help you land a better job. Setting a goal for your career, a certification path to get you there, and some critical milestones along the way will help you stay on track and motivated. Certification in both Big Data tool mastery and Hadoop knowledge may be attained through completion of a Big Data and Hadoop course. By putting your Hadoop knowledge and problem-solving skills to use on real-world projects, you stand out from the competition and increase your employment prospects.