Big data means that a tremendous amount of structured/unstructured data from multiple sources in any form. In recent days, the usage of big data is expansively increasing more in growing technologies. Although big data has dynamic and complex nature, it produces a huge volume of data which are tedious to process in conventional tools and techniques. This article is intended to provide research areas, ideas, and challenges for Big Data Projects with Source Code information!!!

There are 5 important Vs in big data such as variety, volume, value, veracity, and velocity. All these Vs acts as veins of big data in many real-time applications. Here, variety refers to data formats like multimedia, structured and unstructured. Volume refers to data sizes like exabytes, zettabytes, and terabytes. Veracity refers to data truthfulness like incompleteness, inconsistency, and uncertainty. Velocity refers to data generation speed like streaming, batch, and real-time. Here, we have given a detailed view of big data analytics in multiple aspects to give you fundamental information.

Implementing Big Data Projects With Source Code

Comprehensive View of Big Data Analytics 

  • Massive Data Sources
    • Internet of Things
  • Massive Data Analytics
    • Descriptive
    • Prescriptive
    • Predictive
    • Diagnostic
  • Massive Data Pattern
    • Unstructured
    • Semi-structured
    • Structured
  • Massive Data Tools
    • Apache Spark
    • MongoDB
    • Hadoop
    • Cassandra
    • Apache Storm
  • Massive Data System Entities
    • Data Collection
    • Data Analysis
    • Data Transportation
    • Data Maintenance

Now, we can see the lifecycle of the big data system. In this, we have specified the step-by-step instruction for implementing a big data model from preprocessing to recommendations for decision-making. Our developers have long-term experience in handling numerous complex big data projects. So, we are smart and enough to guide you on the right path of project developments. Connect with us to create a masterpiece of your research work in the big data field.

Lifecycle Model for Big Data 

  • At first, collect the raw big data and do perform preprocessing or data integration from the followings,
    • Satellite
    • Sensing
    • Log files
    • Mobile devices
  • Then, filter the essential data by certain conditions or classify the data into unstructured or structured data
  • Next, analyze the data for better understanding or visualization through various tools, technologies, and techniques as follows,
    • Indexing
    • Statistical
    • Cluster
    • Legacy codes
    • Graphics
    • Correlation
    • Regression
  • Then, store the data for content filtering, reliability, management strategies, partition tolerance, the distributed system through any of the following, 
    • Hadoop
    • Voldemort
    • MapReduce
    • Simple DB
    • Memcache DB
  • Next, distribute the data for representation, legal / ethical specification, and documentation
  • After that, secure the data for accessibility, privacy, governance, and integrity
  • At last, retrieve the data through decision-making and searching

With an intention to precisely handle a large volume of data, artificial intelligence has introduced several methodologies. The main features of these methodologies are accuracy and speed. As well, some of the techniques are computational intelligence, machine learning, data mining, natural language processing, etc. Our developers are proficient enough to guide you in choosing the best big data projects. Some of the important research areas that have more research topics are given as follows,  

Top 3 Research Ideas in Big Data 

  • Natural Language Processing
    • Feature – Classification
      • Technique – Open issue and ICA
    • Feature – POS Ambiguity words 
      • Technique – LIBLINEAR, MNB, and ICA algorithm
    • Feature – Keyword search
      • Technique – Bayesian and Fuzzy
  • Computational intelligence 
    • Feature – Variety and High Volume
      • Technique – Fuzzy-logic based matching algorithm, Swarm intelligence, and EA
    • Feature – Noisy data, Complex data, and Low Veracity
      • Technique – EA and Fuzzy logic
  • Machine learning
    • Features – Learning through unlabelled data
      • Technique – Active learning
    • Features – Flexibility
      • Technique – Deep Learning and Distributed learning
    • Features – Learning from minimum veracity / noisy data, imperfect training samples, and unreliable classification
      • Technique – Fuzzy sets, Active learning, Feature Selection, and Deep Learning

In addition, we have also given you the vital research holes in big data analytics. From the vast collections, we have listed only the top 3 research gaps of big data analytics. In fact, these research gaps gained more attention among the research community. Our researchers have gained so many research solutions for different research gaps. We ensure you our solutions are more appropriate for handpicked research problems than others. To know more research gaps that are waiting to create masterwork in the big data research field, communicate with us. 

Research challenges in Big Data Analytics 

  • Quick data processing and analysis
  • Misrepresentation and Uncertainty of data
  • The large data storage system 

Our research team has enough knowledge to cope with all technical issues of big data analytics. Since we have developed countless projects in different big data research areas. So, we are capable to recognize suitable research solutions like algorithms and techniques. As well, we also find the solutions for these research gaps. Once you connect with us, we will help you to identify the appropriate solving-solutions. 

For illustration purposes, here we have taken “Uncertainty issues in big data system” as an example. Uncertainty means faultiness or unknown data which occurs in all the phases of sources. For instance: the data collection phase may include uncertainties due to changing environmental conditions and modality due to noise and complexity. Here, we have given you some modern techniques/algorithms that are more apt for solving uncertainty research problems in efficient ways.  

Emerging Methods for Big Data Analytics

  • Fuzziness
    • Unassured precision 
    • Simple data generation and interpretation 
    • Manage ambiguous data 
  • Rough Set Theory
    • Handle vague and complex data 
    • Utilize only given information
    • Utilize low data for setting membership
    • Offer objective analysis
  • Shannon, Probability and Bayesian Theory
    • Manage complex data
    • Handle subjective uncertainty and randomness
  • Classification Entropy
    • Manage uncertainty among classes
  • Belief Function
    • Consider accessible pieces of evidence for hypothesis
    • Enhances uncertainty reduction but complex in computation
    • Manage situations through a certain degree of ignorance
    • Merge various evidence from multiple sources to determine hypothesis probability
    • Suits for complex and incomplete data

How to design the best big data model? 

           If you are designing a new big data model, then the model needs to be optimized by following characteristics for better performance and efficiency. When you confirm with your project topics, we will find all possible aspects to enhance the big data model while developing. We use the best result-yielding approaches to make the big data model more efficient than others. Let’s have look at some key approaches to improve system performance. 

  • Handle with Large Volume of Datasets
    • The big data collection like complex statistical approaches enable a data analyst to process the data deeply as much as possible 
  • Model Accepts All Types of Data Sources
    • Majorly, the big data system receives and requests the data from all available data sources. Similarly, the EDW technique requests data sources with caution for enabling structured data
  • Uses Big Data Storage Model
    • By the by, the tremendous growth of data sources and data make the demand for fast data storage systems. So, the data analyst can easily generate and access the data in a secure way

Furthermore, our developers have given you some important development tools for implementing big data analytics projects. On knowing the significance of big data, several tools are developed. For your information, here we have listed only a few of them which produce accurate results. In this, each tool is specialized in some aspects and has unique characteristics. Depending on the usage of tools and project requirements, the best-fitting tool is needed to be selected.  

Top Trendy Big Data Tools 

  • Kafka – Data integration and Messaging 
  • Oozie – Task scheduling
  • Pig – Scripting
  • HBASE – Quick read/write accessibility
  • HDFS – Storage and replication
  • Mahout – Machine learning
  • ZooKeeper – Coordination
  • Hive – SQL
  • HCatalog – Metadata
  • MapReduce – Distributed processing 

Moreover, we have also given you some important project ideas for big data projects. These ideas are gathered from the top research areas of big data. In this, we have classified the data into three classifications such as advanced, intermediate, and beginner. Based on the level of advancements, the project ideas are classified. When the advancement is growing, the complexity may also increase. Our developers will provide the best guidance in project development at the level of complexity. 

Big Data Project Ideas 

  • Advanced
    • Brain Tumour Segmentation
    • Online Payment Fraud Detection
    • Opinion-based Customer Classification
  • Intermediate
    • User Data Analysis
    • Driver Sleepiness Detection 
    • Age and Gender Identification
  • Beginner
    • False News Identification
    • Parkinson’s Disease Detection
    • Human Emotion Analysis

Besides, here we have given you the benefits of utilizing big data projects with source code. These benefits are common for all kinds of real-time and non-real-time applications in the big data research field. Further, there are more benefits to implementing big data projects. Below are the major benefits of our big data projects source code:

  • Simple to learn and develop source code 
  • Rapid custom-based applications or services 
  • Consist of numerous amounts of project topics 
  • Applied skills are better than general theoretical aspects 
  • Low cost for project implementation and deployment 

Last but not least, now we can see the list of big data projects with source code. The following projects have source code for immediate delivery. Also, these projects are handpicked from our up-to-date project repositories. So, communicate with us to know other important projects that have source code. We will let you know about other emerging big data projects with source code.

Big Data Projects for students with source code implementation

Top 6 Big Data Projects Source Code (Reach us for Complete Documentation)

Apache Pig Projects

Integration of Impala, Pig, Hive, and Hadoop for Airline Dataset Analysis 

  • Execute the massive data analysis on airline dataset using impala, hive, Hadoop and pig

Forecast the song preferences by processing Million Song Dataset 

  • Analyze the associated worldwide cultures and artists for songs identification 

Apache Hadoop Projects

KSQL-assisted Streaming ETL in Kafka based on NYC TLC Data

  • Understand the way of constructing ETL pipeline on streaming datasets based on Kafka

Simple Model to IoT Ready Infrastructure 

  • Construct an argument for common streaming architecture which uses microservice architecture for reactive data

Applying gradually varying dimensions in Data Warehouse using Spark and Hive 

  • Interpretation of SCDs varieties and apply gradually varying dimensions in spark and hive

Apache Hive Projects

Spark SQL for processing big data 

  • Utilize apache-spark SQL for data distribution and accessibility 

Recommendation of the movie by Movielens dataset analysis based on Spark in Azure

  • Implant pipelines and azure data factory for visualizing the data analysis 
  • Then, recommend the movie using Spark SQL

Modeling of Data Warehouse for Real-World Environs

  • Design a modern data warehouse for real-world environs

Overall, we are here to support you in every development step of your big data projects with research assistance. In research assistance, we help you to handpick the best research topics, research problems, and corresponding research solutions from significant research areas of big data. In development assistance, we help you to choose the best development tool, platform, programming language, data set, and performance metrics with code execution service. Further, we also provide manuscript writing support for your completed big data project.