Big Data is a term that describes a large amount of data, whether structured, semi-structured, unstructured, or raw. On the balance sheet, data could be classified as an asset.
Big Data, according to Gartner, is defined as a large volume, velocity, and variety of data assets that necessitate cost-effective, innovative forms of data processing in order to improve insights and decision-making. As a result, the 3 Vs of Big Data have become widely accepted:
Recent studies, however, have added two more components to the Big Data definition:
5 Vs of Big Data
- Volume refers to the amount of data stored.
- Velocity refers to the rate at which data is sent and received.
- Unstructured text documents, pictures, video, email, audio, stock ticker data, financial transactions, and other data types and sources are all examples of variety.
- Variability: The data flow can be highly inconsistent at times, with periodic peaks that obstruct the process of effectively handling and managing data.
- Data management becomes a difficult task when large volumes of data come from multiple sources.
Indeed, the data sets are so large and complex that processing them with traditional data processing applications becomes extremely difficult and time-consuming. Every day, 2.5 quintillion bytes of data are generated, according to estimates.
This means that in the last two years, roughly 90% of all data in the world was created. It’s worth noting that about 80% of the total data is unstructured – this includes information gathered from weather sensors, social media posts, digital photos and videos, purchase transaction records, and mobile phone GPS, among other sources.
Big Data has been used to improve productivity in both the public and private sectors. In Barack Obama’s re-election campaign in 2012, big data analytics played a crucial role. The Obama administration announced the Big Data Research and Development initiative in 2012, after witnessing the role of Big Data in addressing government problems. Six of the world’s ten most powerful supercomputers are owned by the United States federal government.
Facebook uses Big Data to manage 50 billion photos from its user base in the private sector. Every day, Amazon.com processes millions of back-end operations using Linux-based technology. eBay.com uses a 40 PB Hadoop cluster for search, as well as two 7.5 PB and 40 PB data warehouses. The FICO Falcon Credit Card Fraud Detection System protects 2.1 billion active credit card accounts around the world.
Every hour, Walmart processes more than a million customer transactions, which are imported into databases with a total capacity of more than 2.5 petabytes. Global data volume doubles every 1.2 years, according to estimates.
Role of Big Data in an Enterprise
The development of Big Data databases has allowed businesses to recognize the value of data in their expansion and success. These databases have aided businesses in saving money, increasing revenue, and achieving a variety of other goals. The real challenge for businesses is locating that critical piece of data that gives them a competitive advantage. Hadoop aids in the management and processing of large amounts of data. It also aids in the transformation of data into a more usable structure and format, as well as the extraction of useful analytics.
Big Data in International Development
Big Data’s application is not limited to IT companies or any particular industry. Big Data technologies, according to the Research on Effective Uses of Information and Communication Technologies for Development (ICT4D), can be extremely useful and contribute significantly to solving challenges in international development. Advances in Big Data technologies result in the creation of cost-effective opportunities for improving decision-making in critical areas of development such as healthcare, employment, law and crime, security, natural disasters, and so on.
Big Data Job Opportunities
If you have the right qualifications, Big Data can open up a lot of job opportunities in the IT industry. According to a 2011 study by Mckinsey & Company, the United States may face a severe shortage of people with deep analytical skills in Big Data. Companies are looking for skilled people who can help them take advantage of Big Data’s promise of competitive advantage, and they will continue to do so. Several Big Data jobs necessitate the expertise of experts.
1. Chief Data Officer
An individual is in charge of an organization’s overall Big Data implementation and execution. In the organization, he or she holds a key position. He or she should be a member of an organization’s executive board, reporting to the CEO directly.
2. Big Data Engineer
Engineers who develop, maintain, test, and evaluate Big Data solutions are in high demand. He or she must be well-versed in a variety of programming and scripting languages, such as Java, C++, PHP, Ruby, Python, and other similar languages. Another important skill he must have is the ability to build data processing systems with Hadoop and Hive.
With all of this said, it can be said that Big Data is becoming more mainstream in today’s tech-savvy world, with more and more businesses investing in it to save time and effort while still achieving business success.
3. Data Scientist
One of the most in-demand jobs in the twenty-first century will be this one. The demand for Data Scientists is higher than ever as the Big Data and Data Science industries continue to grow at a breakneck pace. This is not, however, a simple task. Natural learning processes, machine learning, conceptual modeling, statistical analysis, predictive modeling, and hypothetical testing are just a few of the specialized skills required to become a successful Data Scientist.
To be a successful Data Scientist, one must also be proficient in the following skills:
- Strong written and verbal communication skills in a fast-paced, multi-disciplinary environment
- Possess the ability to create program databases.
- Ability to perform statistical analyses and query databases
- Demonstration and example-making abilities
- Ability to work independently
- Design and architecture principles are well-understood.
4. Big Data Analyst
The Data Scientist relies on the assistance of a Big Data Analyst to complete the tasks at hand. His primary responsibilities include working with data in a system and analyzing various data sets. A Data Scientist could be the next position for a Big Data Analyst. As a result, he’ll need to have similar abilities and skills. Advanced modeling techniques, testing, creating, and explaining data in clear and concise reports are among the skills required. For the successful analysis of databases, a Big Data Analyst’s testing skills are crucial. A successful communicator is required of a Big Data Analyst, as he must communicate complex findings and ideas in much simpler terms.
5. Big Data Visualizer
6. Big Data Manager
The Big Data Manager serves as a link between the technical team and the organization’s strategic management. He or she is in charge of leading and managing the Data Scientists, Big Data Analysts, and Big Data Visualizers teams. He or she must master core management skills such as communicating effectively and efficiently, forming personal relationships with the Big Data team, adapting to changing environments, and understanding, interpreting, and relating the organization’s strategy to the team.
7. Big Data Solutions Architect
This domain focuses on specific Big Data issues and requirements. Because they are trained to describe the structure and behavior of a Big Data solution, Big Data Solutions Architects are very important for an organization. Hadoop is a must-know for him. Ability to clearly articulate the benefits and drawbacks of various technologies, ability to document used cases, solutions, and recommendations, strong written and verbal communication skills, self-starter, ability to work in a team, and so on are just a few of the skills required of a Big Data Solutions Architect.