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Big Data is the huge, diversified data volume that keeps growing at an exponential rate, coming from different sources both in structured, unstructured and semi-structured forms. Businesses evaluate this data to gain insights to make better business decisions that otherwise would have been impossible in the present age of technology and business landscape. But it is not the quantity of data that is important. The proper use of it for gaining a perception of what businesses do with such data is important. Big Data influences an individual’s daily life as well as it is experienced every day by way of social media, shopping, entertainment, health, and hordes of other things.
Big Data is characterized by the “three V’s” denoting Volume, velocity or the speed with which it gets created/collected, variety or the various types of data.
Examples of Big Data: Data from social media (texts, numeric, images, video, comments), data from IoTs (internet of things) and others.
A Big Data project is the work of data analysis that uses a variety of very large raw data sets as the foundation for its analysis. Such Big Data analytics projects combine both traditional data analysis techniques and also modern ones that are specifically designed to handle large data volumes. Big Data projects typically use deep learning, machine learning, convolutional neural networks (the deep learning algorithms that take images as input for image recognition and classification), and computer vision as a part of the data analysis process.
Some interesting Big Data project names include Big Data for cybersecurity, anomaly detection in cloud servers, malicious user detection in Big Data collection, tourist behavior analysis.
The data engineers working on any Big Data domain project or Big Data related projects must first acquire proficiency in areas like machine learning, data visualization, data analytics, deep learning etc, and also pursue Big Data Certification Courses to enhance their skills.
GitHub and ProjectPro are some of the platform examples that offer different types of Big Data projects list, including Big Data simple projects, easy Big Data projects, and also Big Data small projects meant for professionals at different skill levels - beginner, intermediate, and advanced.
Some Big Data sample projects examples are:
Source Code: Fruit Image Classification
Source Code- Criminal Network Analysis
The goal of any Big Data analysis project is to do data mining (the process of searching large datasets for patterns, like weather forecasting that analyzes historical data to identify patterns and predict future weather conditions) and analyze the data to uncover underlying patterns and derive insights. Big Data project examples include e-commerce or banking sectors that use Big Data projects to understand customer behavior, trends and formulate business strategies aligned to the insight received from the project.
The steps of a Big Data project are as follows:
Understanding the business or the industry is the foundation of any good Big Data analytics project. This includes:
Gathering raw data from various sources is the next step. Some options for data collection could be:
This is the most time-consuming step of the project. This includes:
Now the cleaned data needs to be transformed or modified to extract useful information. This includes:
This involves data analysis by creating beautiful dashboards, charts, or graphs that data visualization tools offer. This is especially important when massive volumes of data analysis need to be showcased. For example, after plotting data on a map, it could come into focus that some small geographic regions provide more information than some large cities or even nations. This is only possible if proper data visualization is done.
This is the last step of a Big Data project and entails a) an analysis to reveal certain patterns in the data or b) helping solve a specific business challenge.
The analysis results are then presented with the help of various visualization tools to make it easy to understand by everyone.
Big Data projects are crucial to companies. Here are some statistics:
Businesses learn everything about their customers like what they want, who their best customers are, customer behaviors, why people choose different products and plenty of other such information.
By 2025 the world is going to generate 181 zettabytes of data, as per predictions. This could be a gold mine for businesses that can successfully decipher Big Data and extract actionable insights. The more information or insight a company gets, the more competitive it becomes in the market. In the long run, it improves its balance sheet and profitability.
Big Data project insights can be combined with machine learning to create market strategies and become more customer centric.
We will explore some Big Data projects with source code that you could explore and do as well to include in your data science portfolio. We will cover Big Data projects for beginners, intermediate and advanced levels so that you can choose the one that is right for you.
1. Beginners Level
We are discussing here a couple of Big Data analytics projects with source code for beginners. Beginners can learn Big Data Analytics Online. They can use them as Big Data practice projects.
The requirement:
<Source Code – Data Warehouse Design>
The requirement:
<Source Code – Search Engine>
The best Big Data projects will have the following attributes:
The importance of an end- to- end Big Data project does not lie in quantity but in how much meaningful insight a business derives out of it that can support business objectives and inform business decisions.
This calls for scrutinizing a variety of data sources to obtain raw data from, choosing the right algorithms to process it and interpreting the results correctly.
The main objective of any Big Data based projects is the quality of the insight derived from it and its impact on the business to meet the business objectives. Maximizing value and not volume (or using fanciful technologies) should alone be the clear focus of any Big Data project. This is the reason why a good Big Data engineer also needs to be business savvy with the ability to combine technical jargon with business understanding and strategy.
Any good Big Data project or any Big Data major projects will have clean and accurate coding that is with the right formatting and comments kept wherever required. This makes it easy to understand for all involved in the project. The analysis must be free from biases and emotions to make it perfectly accurate.
A data analyst might come across quite a few challenges while executing Big Data projects, especially the Big Data live projects or some real time projects on Big Data. These are:
While working with Big Data real-time projects, monitoring real-time environments could be a problem as not many solutions are available for this.
Output latency during data virtualization is a common problem faced during data analysis due to the tools requiring high-level performance leading to latency in output generation.
While dealing with data, data privacy and the governance policy of the company needs to be adhered to as any privacy breach to it might be fatal to the project.
A Big Data analytics project might require a higher-level of scripting or the use of tools that you are not familiar with.
Jobs in the data science field will increase nearly by 28% to 30% as per the technology predictions of 2022, creating nearly eleven million new jobs. Business leaders who will explore and efficiently utilize the colossal benefits of Big Data insights will remain ahead of their competitors. New roles will come up to close the gap between high demand and low supply of professionals, especially in sectors where the demand is high. Those who will be growing their skills in Big Data will experience a high trajectory in their career growth.
It depends on various factors like data volume to be handled, project team size and processing time. Typically it might take between two weeks to six months.
Most definitely yes! Also, ensure to upload your code to platforms like Github or others for recruiters to see.
Gartner 2017, mentioned 85% of Big Data projects fail
Every industry uses Big Data now, including government, space, defense, healthcare, banking and financial enterprises and e-commerce just to name a few.
Volume, velocity, variety, variability, veracity, and visualization.
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