Big data and Artificial Intelligence have been thriving in recent years, and the emphasis on these technologies will propel them to new heights. Companies have realized the value of big data, and various opportunities are knocking on your door. It is the ideal moment to begin working on your big data project if you are a big data student in your final year. Current suggestions for your next big data project are provided in this article. You can check out the best Big Data courses to have an in-depth idea about big data tools and technologies to prepare for a job in the domain. This article will provide big data project examples, big data projects for final year students , data mini projects with source code and some big data sample projects. The article will also discuss some big data projects using Hadoop and big data projects using Spark .
Let's check some big data analytics projects and big data analytics projects with source code . The top big data projects that you shouldn't miss are listed below.
Applying what you've learned will be necessary. Working on big data projects will allow you to exercise your big data skills. The chance to put your skills to the test is greatly enhanced by projects. Additionally, they look fantastic on resumes. In this article, we'll talk about some fantastic big data project ideas you may work on to show off your expertise in the field. Let’s check some big data projects with source code.
| Beginners | Intermediate | Advanced |
| Traffic control using Big Data | Big Data Cybersecurity | Anomaly detection in Cloud Servers |
| Search Engine | Crime Detection | Smart cities using Big Data |
| Medical insurance fraud detection | Disease prediction based on symptom | Tourist behavior analysis |
| Data warehouse design for an E-Commerce site | Recommendation System | Web Server Log analysis |
The following is a list of some of the best big data projects for beginners:
Many big cities experience traffic problems, particularly during some of the busier times of the day. It may be possible to take action to ease traffic on some roads if popular and alternative routes are continuously checked for traffic. There are many uses and benefits for real-time traffic simulation and prediction projects using big data. Simulating real-time traffic has successfully been modeled.
This project is a Lambda Architecture program that tracks Chicago's streets' traffic conditions, including congestion and safety. On 1,250 roadway segments inside the city limits, it shows current traffic crashes, red-light, and speed camera offenses, as well as traffic trends.
Search engines must manage trillions of network objects and keep track of billions of users' online activities in order to understand what people are searching for. Search engines transform website content into quantitative data. This is an intriguing big data Hadoop project for newcomers who wish to learn the fundamentals of running data queries and analytics using Apache Hive. For obtaining data from various Hadoop-integrated databases and file systems, Hive has a SQL-like interface. If you are familiar with SQL, you should have no trouble completing this project.
Medical Insurance Fraud Detection is a special data science approach for predicting fraud in the medical insurance market that makes use of real-time analysis and classification algorithms. The government can use this tool to help patients, pharmacies, and physicians, ultimately boosting sector trust, addressing the problem of rising healthcare costs, and reducing the effects of fraud. With the help of data scientists and workers with AI backgrounds, this project uses data analytics in a special way to uncover connections between healthcare professionals.
In this big data project, you will be building a data warehouse for a retail establishment. However, it focuses on providing answers to a few specific questions on the design and implementation of pricing optimization and inventory allocation. You'll be attempting to respond to the following two questions in this hive project:
The following is a list of some of the best intermediate big data projects:
It is among the important big data machine learning projects . By obtaining login credentials from any of the company's users and then getting into the network, cyber attackers may choose to target a particular company. It is very challenging for ordinary antivirus software to detect this, given that the user credentials are genuine and that a cyberattack may occur without anyone being aware of it. Your user behavior modeling system will be built using big data algorithms.
The main goal of this Big Data project is to use sophisticated multivariate time series data to manipulate vulnerability disclosure trends in current cybersecurity issues. The system's machine learning and automation engines are integrated with outliers and detect suspicious technologies based on Hadoop, Spark, and Storm, allowing for real-time detection of fraud and prevention of threats in forensics.
It is among the important Apache big data projects. This intriguing big data study looks for trends to anticipate and identify connections in a dynamic criminal network. Since the criminal network is a dynamic social graph, this study uses a stream processing technique to extract pertinent information as soon as data is generated. Additionally, it offers three brand-new social network similarity indicators for the detection and forecasting of criminal links. The following phase entails creating a flexible data stream analysis application with the Apache Flink framework, allowing for the deployment and assessment of both newly proposed and existing metrics.
There's a phrase that goes, "Health is wealth." To be fair, wealth cannot exist unless one is well enough to engage in worldly pleasures. Risk factors for many diseases can be genetic, environmental, or nutritional, more prevalent in a certain age group or sex, and more prevalent in various races or regions. They can also be environmental or genetic.
The presence of additional risk variables can be used to calculate the likelihood that a certain disease would manifest by compiling datasets of this information that are pertinent for specific conditions, such as diabetes, Parkinson's disease, and breast cancer. When the risk variables are unknown, the datasets can be analyzed to find patterns of risk factors and, as a result, forecast the likelihood of onset appropriately.
Online services often provide access to thousands, millions, or even billions of items, including goods, advertisements, video clips, movies, music, blog entries, and so forth. Big data makes it possible for recommendation systems to give accurate and pertinent recommendations by providing a wealth of user data, including past purchases, browsing history, and opinions. Our recommendation system for mini-movies is powered by big data. This project aims to compare how different recommendation models function on the Hadoop Framework.
The following is a list of some of the advanced-level Big Data projects :
As cloud computing has grown in popularity, many people and businesses have turned to cloud storage solutions. This approach is prompted by benefits like shared storage, computing, and transparent service among a large number of users. However, maintaining sophisticated, large-scale systems with essentially inescapable runtime issues brought on by hardware and software errors is necessary for cloud computing systems. A crucial strategy for handling such complicated cloud resources is automatic anomaly detection.
Smart cities are technologically advanced urban centers that gather data through the use of various digital means, voice activation methods, and sensors. The knowledge gained from the data is used to manage resources, services, and assets effectively; in turn, the data is used to enhance operations across the city.
A nation's economy might be negatively impacted by the enormous industry of tourism, which supports the livelihoods of many people. This behavior can be examined in terms of decision-making, perception, destination preference, and level of satisfaction to ensure that both visitors and residents have a positive experience. One of the more sophisticated project concepts in the Big Data space is behavior analysis, which is similar to sentiment analysis.
Web server log analysis can be used to acquire a feel of the overall user experience. Any business that depends heavily on its website for customer service or revenue production can benefit from this type of processing.
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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
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:
1. Inadequate Monitoring: While working with Big Data real-time projects, monitoring real-time environments could be a problem as not many solutions are available for this.
2. Latency Problems: 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.
3. Data Privacy: 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.
4. Demanding Scripts/ Tools: A Big Data analytics project might require a higher-level of scripting or the use of tools that you are not familiar with.
A big data project is a data analysis program that bases its analysis on a very sizable data set. Big data is any collection of data that is larger than one terabyte.
Traditional data analysis methods are combined with others that are specifically designed to manage high data volumes in big data initiatives. Big data engineers frequently use deep learning, machine learning, and computer vision as part of their analytical process.
Because of the limitations of conventional techniques, software engineers could not truly analyze very large volumes of data before the development of the big data area. The future of project big data is bright, and here are some of the examples that tell us why big data is important:
Thus, the article provides a concise big data projects list and various big data-related projects. Big data is already enormous, but it is predicted to increase rapidly as new technologies enter the picture, like the increasingly prevalent IoT devices, drones, and wearables. You can enroll in the KnowledgeHut best Big Data courses to learn important concepts and aspects of big data from industry experts to launch a successful career in Big Data.
Data Projects are initiatives to people whose goal is to deliver something useful that can be used. This could involve developing and writing reports, using machine learning models, and other activities.
A big data project is a data management project that bases its analysis on a very large data set.
Having a good project plan is the first and most important stage in starting any project endeavor. A well-defined procedure should always be followed while developing a large data project.
A big data project's objective is to be able to mine data and analyze it to find hidden patterns. Big data is used by today's data-driven businesses to better understand their customers and inform corporate strategy, such as those in the banking and e-commerce industries.
