Currently, the world that we live in is ruled by data. Hence, most individuals are opting to undergo some data analytics training to enter this billion-dollar industry. You can choose to be a Data Scientist, Engineer, or Analyst; you need to have proper training and certification that will help you fast-track your career and apply for top-paying jobs.
As mentioned above, it’s not surprising that analyzing data plays a crucial role as we’re living in a data-driven world where 90% of all data created in the last two years alone. That being said, while most enthusiasts are ready to jump the wagon and join this billion-dollar industry, there are certain things you should know before enrolling for the Big Data Course:
What Is Big Data?
Big Data is essentially data but in a large amount. The term Big Data is used to describe a huge collection of data, which is expected to keep growing with time. Due to a large amount of data, none of the commonly used data management tools are able to analyze or store and process the data effectively.
That being said, to get an in-depth understanding of what exactly is Big Data, some historic definitions can be used. For example, in 2001, Gartner released a definition of Big Data, which is still being used to date. The definition states, “Big data is data that contains greater Variety arriving in increasing volumes and with ever-higher velocity. This is known as the six Vs.”
The Six Vs of Big Data
1. Volume
The first to consider when you receive data is to analyze how much of it is there? When it comes to Big Data, high volumes of low-density, and unstructured data needs to be processed and analyzed. As it is unstructured, this data can be unknown, for example, Twitter data feeds, clickstreams on a web page, and others. For certain organizations, depending on the niche they operate in, storing such kind of data amount to more than tens of terabytes of data or even hundreds of petabytes.
2. Velocity
The next V stands for velocity that indicates the rate at which the data is received on an average. Generally, this highest velocity of data streams directly into memory, instead of being written on the disk. On the other hand, when it comes to smart products that are enabled by the internet, the data will be processed in real-time or near real-time, and hence, this type of data required real-time evaluation and action.
3. Variety
The third V stands for Variety, which is basically the different types of data. The most common types of data are structured and unstructured. Structured data is more traditional and preferred as it fits neatly in a relational database. However, as the applications of big data keep increasing, data started becoming unstructured. When it comes to unstructured data, examples like text or audio could require additional preprocessing, which will help decode the meaning of the data and support metadata.
4. Value
To put it simply, data has no intrinsic value. Data only proves to be valuable if a company is able to extract relevant insights to solve a particular problem or meet a specific need. Hence, data acquired value through the impact it leaves on a business and the consumer value insights the data delivers. In order to harness the full value of data, organizations can follow the steps given below:
- Ensure you spend enough time analyzing the data by digging deep to derive clear insights, based on which you can develop a strategy
- Closely monitor any changes in regulations, especially when it comes to security requirements
- Analyze how you can be more transparent with your customers regarding the usage of their data
5. Veracity
The term veracity, when it comes to big data, refers to ensuring the reliability and validity of the insights derived from the data analyzed. This is particularly important as if the data is inaccurate, it is not only useless, but can also result in major repercussions if applied. However, maintaining a balance is crucial as in the quest to obtain veracity you might be over-cautious and wait for perfect, clean data before making any decision – which is extremely impractical.
6. Variability
The last V is for variability, which is the most debated upon concept as it can refer to multiple things. Mostly, variability stands for the inconsistencies in the data that need to be found by anomaly and outlier detection methods. Variability could also refer to the inconsistent speed at which big data is loaded into your database.
The Scope of Big Data in 2020
According to a report published by IDC, the Worldwide Big Data & Business Analytics Market is estimated to grow from $130.1 billion this year to over $203 billion in 2020. The report went on to quote IDC’s Dan Vesset, where he stated that due to the easy access to data, the introduction of the latest technology and colossal culture shift towards making data-driven decisions, the drive for big data and analytics technology and services.
Based on the current growth rate of this big data industry, given below, are a few predictions of the scope of big data in 2020:
1. Ever-Increasing Demand for Data Analytics
Fairly recently, Peter Sondergaard, who was employed at Gartner Research, where he released a statement about Big Data and the importance of data analytics in the modern world. He said that information is the oil of the 21st century, and analytics of that information is the combustion engine.
That being said, the combustion engine is an integral part. Hence, regardless of the tons of the data we are collecting every minute, it is essential that we can understand the data with some data analytics skills. The main question of the hour essentially is, who is responsible for analyzing vast quantities of data and transforming them into valuable business?
2. Applications of Big Data Is Across Different Parallels
Big Data is often considered to be omnipresent, and hence big data has multiple applications across different sectors of industry. According to a study by Wanted Analytics (2015), the biggest significant demand for Big Data professionals is by Professional, Scientific and Technical Services (25%), Information Technology (17%), Manufacturing (15%), Finance and Insurance (9%), and Retail Trade (8%).
3. Career Opportunity & Salary Growth
Due to the different requirements involved, Big Data provides the most versatile career options for those who are looking to join the industry.
Moreover, as most companies, even tech giants like IBM, Microsoft, Oracle, Google, and Pentaho, are making use of the data derived from analyzing Big Data. This ultimately results in increased job opportunities for skilled professionals.
Moreover, as the demand for Big Data grows, the salary package offered to the professionals of this industry grows as well. When it comes to Big Data jobs in India, a fresher with a Master’s degree in Data Science or Analytics can land a job with a package between INR 4 – 10 LPA, depending on their skill set and the company they decide to join. On the other hand, candidates who have 3-6 years of experience can bag a package of up to INR 10 – 20 LPA. Moving to the more senior professionals of the field, employees with over 6-10 years of experience earn about INR 15 – 30 LPA.
Job Opportunities in Big Data
With almost every company looking to benefit from Big Data, professionals who have experience and knowledge in this field are in high demand. The top job opportunities in Big Data include:
1. Data Scientist
There are plenty of opportunities for professionals who are capable of mining and interpreting complex data in large volumes. Essentially, Data Scientists partners with cross-functional IT teams to compile and analyze data and derive insights that are ultimately presented in the form of recommendations and action plans.
2. Data Engineer
A Data Engineer combines concepts from computer science and engineering to analyze and manipulate large volumes of data. Everyday tasks of a Data Engineer include creating and translating computer algorithms into prototype code, developing technical processes to improve data accessibility, and designing reports, dashboards, and tools for end-users.
3. Data Analyst
The job description of a Data Analyst is to gather insights about various topics by creating large-scale surveys. Their job entails recruiting participants, compiling and analyzing the data received and converting the actionable points into traditional charts and reports.
4. Security Engineer
One of the core jobs of a Security Engineer is to plan, avert, and mitigate IT disasters. With the help of computer firewalls, detecting and responding to intrusions, and pinpointing security issues in the system, a Security Engineer is responsible for lessening corporate risk. They are also in charge of creating and implementing test plans for new software and hardware to implement adequate security measures from the beginning.
5. Database Manager
A Database Manager is responsible for managing, performing diagnostics, and repairing databases, whenever required. They are also responsible for reviewing business requests for data, tracking the use of data, and verifying data sources to improve the quality of the data feed.
Skills Required To Get A Big Data Job
As the market is extremely competitive, you need certain skills to ensure you can land a job in this niche. Mentioned below are a couple of skills that can help:
1. Apache Hadoop
Hadoop has entered its second decade, but there has been a steep rise in its popularity in the last 3-4 years. Many software companies commonly use Hadoop clusters. Standard Hadoop components include Hive, Pig, HDFS, HBase, MapReduce, etc. are extremely popular too. Hence, professionals looking to enter the Big Data niche should be proficient with this technology.
2. NoSQL
Traditional SQL database like DB2, Oracle, and others have been replaced by NoSQL databases like Couchbase and MongoDB. These databases are handy when it comes to meeting the needs of big data storage and easy access. NoSQL complements that expertise of Hadoop, and professionals who are well-versed with this technology are sure to find multiple opportunities.
3. Data Visualisation
Tools like QlikView and Tableau that are part of the data visualization are popularly used to decode valuable insights from the data provided from analytics tools. However, using these tools is complex and tough to grasp. Hence, professionals who know how to use them effectively are in high demand, especially in big organizations.
4. Machine Learning
The hottest fields in Big Data currently are Machine Learning and Data Mining. Even though Big Data comprises of multiple components, these two play an essential role when it comes to the success of this field. Professionals who are knowledgeable about machine learning can use their skills to carry out a predictive and prescriptive analysis. As this skill set is rare, professionals with the knowledge of machine learning and data mining are paid exceptionally well.
5. Apache Spark
Replacing complex technologies like MapReduce, Spark is a preferred alternative as it is easy to use and quicker. With its latest developments, Apache Spart has become very popular, with or without Hadoop. With many companies embracing this Apache technology, professionals are proficient with these skills can find multiple high-paying opportunities.
Advantages of Big Data Certifications
If you don’t have the skills mentioned above, don’t fret. Certifications are a great way to make a career shift, and the Big Data niche is no different. Especially in this fast-changing world of technology, with new concepts and technologies being invented almost daily, certifications have become a reliable way to prove competency to companies. Basically, certifications act like a speedy and practical crash course, that allows you to enhance your skill set. Mentioned below are a few certifications you could consider opting for:
1. HortonWorks Certification on Hadoop Developer and Administrator
A new player in the Hadoop distribution market, Hortonworks was founded as a spun-off from Yahoo in 2011 and maintains the Hadoop infrastructure in-house. Currently, HortonWorks is the only vendor that distributes an open-source Hadoop without any cost for any other software.
2. IBM Certified Data Architect- Big data
If you’re looking to pursue your career as a Big Data Architect, you need to have extensive knowledge about technologies and tools and how they can be integrated to solve Big Data business problems. IBM’s website provides detailed information about the skills and knowledge required before giving the exam. The website also provides a list of courses you can choose from.
3. SAS Certified Big Data Professional
To get into the SAS Certified Big Data Professional Program, the candidate must have had at least six months of programming experience, either in SAS or another programming language. It is also essential for you to have knowledge about SQL, Macro, and Advanced Programming.
The academy’s big data certification program focuses on these areas:
- Improving data quality for reporting and analytics
- Fundamentals of statistics and analytics
- Working with Hadoop, Hive, Pig, and SAS
- Exploring and visualizing data
You can read more or apply for this certification from the N.L.Dalmia website.
Conclusion
Regardless of how advanced technology gets, the need for human insights cannot be removed from the equation. There is a serious need for skills professionals who can understand data and derive useful insights from a business point of view. A professional with Analytical skills can master the ocean of Big Data and become a vital asset to an organization, boosting the business and their career.