Learn what big data is, role of a big data engineer, applications of big data in real life, and how does it work.
What is Big Data?
Big data is a term that is used to describe large amounts of data in structured, semistructured and unstructured forms. It contains different varieties of data in one place so that it’s used in machine learning and predictive analysis for organizations. Big data consists of three V’s, i.e., volume, velocity and variability. Big data can be described in a terabyte, petabytes, and exabytes.
What is a Big Data Engineer?
A big data engineer is an IT professional whose responsibilities are designing, building, testing and maintaining big data processing systems.
As a Big Data engineer, there are some responsibilities:
- Building a high-scalable data management system
- Design top-tier algorithms, predictive models and prototypes
- Focus on data quality, reliability and efficiency
- Create datasets for mining, modeling and production
- Work with data architects and IT teams.
These are some key responsibilities of becoming a big data engineer.
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How does Big Data Work?
Big Data is a method of processing big amounts of data and producing valuable results to make meaningful decisions. Many automated systems built for big data processing are used to process big amounts of data, analyze it, and provide the best possible results.
In simple words, Big data processing can be divided into three parts, i.e., collecting, processing and analyzing big data.
This three step process is used to analyze big data and produce valuable results. Let’s discuss this in detail!
1. Collecting Data
As we’ve discussed, big data is a collection of structured, semi structured and unstructured forms of data. So whenever any organization wants to process big data, the first step is to collect data from many sources and in many formats such as audio, video, text, images, documents, GIFs and many other forms of data collected.
2. Processing Data
Once the big amount of data is collected in one place, then it’s time to organize it properly to get accurate results. Basically, there are two steps commonly used for data processing, i.e., batch processing and stream processing. With this method, it’s easy to organize collected data and help analyze the data faster. And before sending to the analyzing, make sure to eliminate duplicate or irrelevant data to get processed.
3. Analyze Data
Now, there are well-organized big data ready to use for analyzing data. Many Organizations are using different analyzing methods to analyze data and produce helpful outcomes:
- Data Mining: Data Mining is used to sort large amounts of data into a specific pattern that easily identifies different data sets.
- Predictive Analysis: This method predicts future needs, opportunities and risks based on analyzing an organization’s historical data.
- Deep Learning: Machine learning and artificial intelligence are used to find valuable patterns for processing big data.
Why is Big Data Important?
1.7 megabytes of data are created each second, according to the study of FinancesOnline.
With such a massive data size, it’s essential to optimize and produce useful information that helps make greater decisions and improve results.
Companies use Big Data analysis to improve their performance and business operations, enhance customer experience and convert marketing campaigns.
For this big data processing, companies are using their historical data. They use big data processing methods to analyze the data and produce useful results that help to make profitable decisions. With previous big data, it’s easy to predict the future opportunities and the demand of customers’ needs and the best possible way to fulfill them. This valuable information helps companies work according to market needs and grow their business.
There are many key reasons for companies to use big data:
1. Cost-Saving
For big data processing, many tools are used, like Apache Hadoop, Spark etc., to store and optimize large amounts of data and produce effective results for a company’s growth.
2. Time-Saving
Big data tools like Hadoop, spark runs faster and help analyze and produce actionable decisions.
3. Understand the market conditions
Big data processing helps businesses understand the market needs and potential customers’ behavior.
4. Boost customers acquisition
Processing big data helps you analyze potential customers’ behavior and effective ways to enhance marketing campaigns that boost customer acquisition.
5. Improve marketing campaigns
Companies are running marketing campaigns and produce lots of data after running many campaigns, then use big data to know the flaws and improve the marketing campaigns based on customers’ behavior.
6. Innovations and Product development
When companies are doing big data processing, it produces many innovations, and customers demand products that help companies make futuristic decisions.
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Big Data Applications
There are many sectors of industry where big data processing is used. Companies are using their historical data and optimization to make valuable decisions.
Here are some of the major areas where big data is used:
1. Tracking customer spending habits and shopping behavior
There are many giant e-commerce companies like Amazon, Walmart, Flipkart etc. that are storing big amounts of data in their data warehouse and later, with big data processing, companies try to analyze whole data and understand their customer’s behavior on their website, shopping behavior and who are potential customers.
2. Recommendations
By using historical data of any organization, big data can be processed and analyzed customers’ behavior and their interests. So that companies can showcase the most relevant recommended products.
3. Smart Traffic System
Big Data is also used to enhance and improve the experience of traffic systems in different locations. Big data processing is important for enhancing the traffic system.
4. Auto Driving Car
Big data analysis also helps drive a car automatically by analyzing surrounding spots like obstacles, distance from the objects and many other things.
These sectors are massively using big data analytics to enhance their performance.
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