Top 5 Intriguing job opportunities in Big Data Technology
Big data is now omnipresent, and there is an urgent need to amalgamate and store the data that is being generated and that is floating around. It has now become imperative to know how to manage and analyse that data. This is also the prime reason why big data is replacing traditional database management applications and processes in IT companies. With 2.5 quintillion bytes ofdata being generated every day, there is a need for a more robust system that can manipulate this extensive volume.
The insights offered by big data have proven to improve business, make better decisions, and drive a distinct edge over the competitors. Analysts across companies are drawing benefits from being fluent with the grammar of big data. Big data jobs aim at collecting and analysing the variety, volume, and velocity of data generated by companies, government, or individuals. A resource trained in the basics of statistics, machine learning tactics, predictive analytics, and data analytics is well suited for opting for a career in big data. Individuals looking to boost their career in the field can undertake big data certification courses.
Big data roles:
Big Data Analyst (BDA)
An upcoming profession that is in demand, with technology giants and asset management firms actively looking forward to hiring BDAs. A Big Data Analyst must be proficient in capturing data, storing, sharing, and transferring data, analysing data, visualising data applications, and regularly updating the data while creating its mutation reports. BDAs are required to be fluent in programming and problem-solving languages such as R, Python, C++, HTML, etc. They should also be able to pose queries to the databases on receiving any requests from the stakeholders. A BDA may also be expected to play the role of a Junior Data scientist wherein the role requires core data mining and visualisation through the creation of user-friendly dashboards.
The roles of a Data Scientist begins with data collection and concludes with giving suggestions to change the existing business strategies and goals. They can read and correlate data sets and draw interpretations from the collected data. There are two types of datasets that the scientist deals with – structured data, and unstructured data. Structured data is tangible and is defined, for example, number of products sold, electronic devices used and the time spent by the user on a website. Unstructured data, on the other hand, comprises of soft data like customer feedback.
Since unstructured data is relatively complex to make sense of, Data Scientists are employed to specifically tackle this variety. They manipulate the unstructured data and derive usable interpretations by running sorting algorithms aimed at specific keywords. The required skill sets of a Data Scientist requires the knowledge of R, SAS, Python, SQL, MATLAB, Hive, Pig, and Spark. Additionally, they need to be knowledgeable about the quantitative and qualitative sampling of data, statistics, mathematics, and logical reasoning.
Hadoop Developer / Architect
Hadoop is a Java-based programming language responsible for storing and processing of large sets of data (big data). Hadoop Developers are responsible for sourcing data from different places through user-defined functions, and for developing supporting Application Processing Interface (APIs). Hadoop Developers are required to have a working knowledge of the Hadoop ecosystem and its components, which includes HBase, Pig, Hive, Sqoop, Flume, and Oozie. They are also required to have a basic know-how of Linux administration and Java essentials on Hadoop. They should also have an understanding of data visualisation tools like Qlikview, Teradata, and Tableau. A Hadoop Developer is similar to a regular software developer, except that the former is responsible for big data manipulation.
Data Engineer / Data Architect
Data Engineers are responsible for the design and management of the big data infrastructure for the company. They are expected to possess strong programming and computational skills, though they need not necessarily know the skills of Machine Learning and Statistics. Data Architects build data manipulation dashboards that facilitate the ease of procurement for data scientists. Small-sized companies expect their data scientists to perform the role of data architects as compared to larger companies that differentiate between the two roles.
Machine Learning (ML) Engineer
A machine learning engineer is a hardcore computer programmer who is sensitive to the growing big data trends. With this knowledge, he then produces an algorithm and implements it to understand the data comprehensively. ML Engineers operate on past data trends and utilise them according to the given objectives. For this position, one must be well versed in languages like Python, C, Java, and Scala. They are also responsible for bringing developments in the field of Artificial Intelligence. They would, for instance, work on developing an algorithm that feeds like a Data Scraper and generates AI. The algorithms perform clustering, classification, prediction, and anomaly detection, etc. to cater to various business challenges.
Big Data, Big Possibilities
For a professional to excel in the field of big data, expertise in Hadoop and NO_SQL is an added advantage. The world will see an average of more than 10% growth in industries that run on big data for the next ten years. However, the availability of people in this domain will not reflect the increase of qualified professionals. Hence, this is the right time to explore and consider futuristic career options in big data.
Peripheral professions in the industry include the roles of Business Analyst, software developer, and database technology engineers. The unique trait that is common among all these professions is deep, intellectual curiosity with natural inquisitiveness. This allows IT professionals to investigate and build things on their own, thus giving them an edge over others in their fields.
Currently, India has the largest reserve of data scientists globally. With most of the international companies outsourcing their analytics reports, there is a heightened demand for data scientists and interpreters. Hence, this is the right time to invest in a big data certification course and enhance these skills.