It was projected that by the year 2020, each person on earth will be creating 1.7 megabytes of information each second. This translates to astoundingly massive volumes of data being generated every day! This represents a profit growth from $122 billion in 2015 to a projected more than double at $274.3 billion in 2022 as businesses integrate big data analytics into their operations. To become a big data engineer today is way more demanding than it was a few years ago.
- There are 40,000 search queries on Google every second. In a year this comes to roughly 1.2 trillion searches.
- There are 500 new video hours on YouTube every minute.
- Facebook generates at least 4 petabytes of data daily which includes 31 million messages sent every minute and more than 2.7 million videos.
- Smart devices produce 5 quintillion bytes (5 exabytes) of data every day.
Industries Using Big Data
While big data has penetrated most industries, some have benefited, are investing in, and restructuring to leverage big data. 5 industries that have invested most in big data include:
- The banking industry. In the banking industry, speedy and personalized services, convenient digital services, effective risk analysis and fraud detection systems, as well as effective customer feedback analysis are some of the improvements that big data is bringing. Big data, it is estimated, will increase revenue by 18%.
- The manufacturing industry. Big data is playing an important role in the automation of processes and maintenance of systems and processes to improve productivity.
- Healthcare. According to Mckinsey, the healthcare sector can save up to 17% of costs by investing in big data analytics. In 2013, this translated to $493 billion. Big data, through predictive analysis, is also helping healthcare providers deliver better and more informed patient care
- Government services. Governments can benefit in many ways by implementing big data in their operations. For instance, checking its workforce, identifying and curbing inefficiencies, reducing waste of resources, and enhancing emergency response among others.
- The retail industry. Both the big players like Amazon, Walmart, and Target and small players are harnessing big data insights to keep a 360-degree view of customer databases for better service delivery and improved customer experience. Big data is also streamlining the pricing of products and business operations.
What Is Big Data?
Big data refers to large and diverse sets of information that keep growing in volume at a fast rate and that cannot be processed by traditional tools and techniques. Big data is defined by the 3Vs, a definition that Doug Laney, an analyst at Meta Group, advanced in 2001. It was later picked up and widely marketed by Gartner in 2005 after acquiring Meta Group.
The 3Vs Refer To:
- Volume. The large volume of information available in the various sectors or industries.
- Variety. Data is of a wide variety coming from many different sources in different formats and could be structured, semistructured, or unstructured.
- Velocity. Data is generated at a very high rate, collected, stored, and analyzed.
Top Roles In Big Data
From 2012, big data has created more than 14 million jobs across the world and the numbers keep soaring. A variety of job roles exist in the big data domain. Some top job roles in this Big Data include:
Big Data Engineer
A big data engineer is the top job commanding the highest salary at $150,000 median. With this comes demanding roles. These professionals oversee the operations of big data systems and applications. They develop technical processes and also build, test and deploy systems designed by data architects. They also work together with data scientists and analysts. They are skilled in working with Hadoop and SQL database tools and are well familiar with Python, Java, and R programming languages.
A data scientist works with data sets, processing them into insights that facilitate decision making. They create visualizations and presentations out of big data that end-users use to make important business decisions. A data scientist needs to be well versed in statistics and math and be able to work with popular programming languages like Java, Python, and R. On the other hand, they need to have good communication and presentation skills. This is the most in-demand job and commands a median salary of $119,000.
Analysts analyze data and make sense out of it. These individuals draw reports from data sets using analytics tools and other data analysis software. Data analysts are skilled in excel, SQL, Hadoop, and other database applications. A data analyst commands a median salary of $96,000.
A data architect is responsible for liaising with business leaders to come up with requirements analysis and then designing the architecture strategy and application solution that match these requirements. These individuals mostly use Hadoop therefore one should be able to use this tool effectively. Additionally, they should know languages like SQL and XML. a data architect will command a median salary of $130,000.
A database developer is a typical developer whose roles are limited to big data databases. They create and deploy database systems. They should be well familiar with writing code and know programming languages like SQL and Python. A database developer earns a median salary of $116,000.
A Big Data Engineer
A data engineer is a professional charged with the responsibility of developing, building, and maintaining database infrastructure and systems. They work hand in hand with the data architect who designs the infrastructure that they build and the data scientist who uses the systems that they build for data analysis.
The Roles Of A Big Data Engineer Include:
- Building and maintaining data pipelines to ensure effective flow and storage of data
- Collecting, processing, visualizing and storing large complex data sets from which business insight can be drawn.
- Securing the business’s databases and data systems
- Make a decision regarding the right software and hardware design based on business requirements.
A bachelor’s degree in computer science, applied mathematics, information science or a related field is the basic requirement for a career in data engineering.
Having relevant certifications will be an added advantage to advancing a career in big data engineering. Certifications equip you with career-specific skills that recruiters are keen on.
- Certified Data Management Professional (CDMP)
- IBM Certified Data Engineer – Big Data
- Google Profesional Data Engineer
- SAS Big Data Professional
Technical Skills Required For Data Engineering
- Programming languages including SQL, Python, Java, and R
- ETL methodologies
- SQL and NoSQL databases
- Familiarity working with cloud platforms like Amazon Web Services, Google Cloud Platform, or Microsoft Azure
- Machine learning and artificial intelligence
- Data mining, structure, and algorithms
- Technical diagramming and data visualization
Data engineering is a rewarding career that is demanding. A formal educational certification alone is not enough to land you a job in this field. You need additional certification training to earn the relevant credentials as well as experience. Additionally, it is important to keep updating your skills as requirements keep changing with changes in technology.