This repository demonstrates big data processing, visualization, and machine learning using tools such as Hadoop, Spark, Kafka, and Python.
1. Python
Description:
Python is a high-level, interpreted programming language known for its readability and versatility. It is widely used in data science for tasks such as data manipulation, analysis, and visualization. Libraries such as Pandas, Matplotlib, and Scikit-Learn provide powerful tools for handling and analyzing large datasets.
2. Hadoop
Description:
Hadoop is an open-source framework that allows for the distributed processing of large datasets across clusters of computers using simple programming models. Its core components include the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing data.
3. MapReduce
Description:
MapReduce is a programming model used for processing and generating large datasets with a parallel, distributed algorithm on a cluster. The model consists of two main tasks:
- Map: Processes input data and produces intermediate key-value pairs.
- Reduce: Merges all intermediate values associated with the same key and outputs the final result.
4. Apache Hive
Description:
Apache Hive is a data warehousing and SQL-like query language for Hadoop. It provides a high-level abstraction over Hadoop's complexity by allowing users to write SQL queries (HiveQL) to interact with data stored in HDFS.
5. Apache Spark
Description:
Apache Spark is a fast, open-source processing engine designed for large-scale data processing. It offers high-level APIs in multiple programming languages and modules for SQL, machine learning, and streaming.
6. Apache Kafka
Description:
Apache Kafka is a distributed streaming platform that enables real-time data pipelines and streaming applications. It is designed for high throughput and fault tolerance, making it ideal for applications that require processing and analyzing continuous streams of data.
7. Matplotlib
Description:
Matplotlib is a comprehensive plotting library for Python that allows users to create static, animated, and interactive visualizations in a variety of formats. It’s widely used for data analysis and scientific computing.
8. Seaborn
Description:
Seaborn is a statistical data visualization library based on Matplotlib. It provides a high-level interface for creating attractive and informative graphics, simplifying the process of creating complex visualizations.
9. Spark MLlib:
Description: Spark MLlib is a scalable machine learning library integrated with Apache Spark, designed to handle large-scale data processing efficiently. It offers a variety of algorithms and utilities for classification, regression, clustering, collaborative filtering, and dimensionality reduction.
10. GraphX:
Description: A component of Spark for graph processing and graph-structured data analytics. GraphX is Apache Spark’s library for graph analytics, providing a unified API for graph-parallel computation. It enables users to analyze, transform, and process graph-structured data effectively.
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Codes 💻 (If applicable)
Contains code files used for the data processing and analysis in each experiment. These files are critical for performing the tasks required in the experiment.- e.g.,
main.py,process_data.py
- e.g.,
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Documentation 📝
This folder contains detailed documentation for each experiment, including methodology, analysis, and insights. Documentation is provided in both Markdown (.md) and PDF formats for easy reference.documentation.md(Markdown version of the documentation)documentation.pdf(PDF version of the documentation)
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Dataset 📁 (If applicable)
Contains the datasets used for analysis in each experiment. Datasets are placed here to ensure easy access and organization.- e.g.,
data.csv,stream_data.json
- e.g.,
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Output 📊
Stores the output generated from each experiment, including visualizations, data analysis results, and any other relevant outputs.Experiment X Output(where "X" refers to the relevant experiment number)
Big-Data-Analytics/
│
├── Experiment 1/
│ ├── Output/ 📊
│ │ └── Contains the results and analysis of Experiment 1.
│
├── Experiment 2/
│ ├── Output/ 📊
│ │ └── Contains the results and analysis of Experiment 2.
│ ├── Commands/ 📋
│ │ └── Lists the commands used during Experiment 2.
│
├── Experiment 3/
│ ├── Codes/ 💻
│ │ └── Contains the code used for data processing in Experiment 3.
│ ├── Output/ 📊
│ │ └── Contains the results and analysis of Experiment 3.
│
├── Experiment 4/
│ ├── Codes/ 💻
│ │ └── Contains the script for processing and visualizing data in Experiment 4.
│ ├── Documentation/ 📝
│ │ ├── Detailed documentation explaining the methodology and analysis for Experiment 4.
│ ├── Output/ 📊
│ │ └── Contains the results and analysis of Experiment 4.
│
├── Experiment 5/
│ ├── Dataset/ 📁
│ │ └── The dataset used for analysis in Experiment 5.
│ ├── Documentation/ 📝
│ │ ├── Comprehensive documentation detailing Experiment 5’s procedures and insights.
│ ├── Output/ 📊
│ │ └── Contains the results and analysis of Experiment 5.
│
└── Experiment 6/
├── Dataset/ 📁
│ └── The streaming data used for analysis in Experiment 6.
├── Documentation/ 📝
│ ├── Explanation of methods and key observations from Experiment 6.
├── Output/ 📊
│ └── Contains the results and analysis of Experiment 6.
.....
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Codes Folder (💻):
Contains the source code used for the experiment. If the experiment involves running scripts or programs, the corresponding code files go here. -
Dataset Folder (📁):
This folder stores the dataset used in an experiment. If a dataset is involved (like a.csv,.json, or any data file), it will be placed here. -
Output Folder (📊):
Stores the outputs/results generated by the experiments. This might include processed data, logs, or result files. Each experiment’s output is stored separately with a relevant name. -
Documentation Folder (📝):
Contains the documentation of each experiment, provided in both.mdand.pdfformats. The Markdown file is converted to PDF using the provided link for Markdown to PDF conversion. -
Commands File (📋):
A text file documenting the specific commands or steps used in the experiment, especially useful for command-line operations.
This experiment involves the installation and setup of Hadoop on your system. It covers the necessary configurations to get Hadoop up and running, enabling exploration of its capabilities for handling large-scale data processing tasks.
In this experiment, we use Hadoop to explore large-scale datasets stored in the Hadoop Distributed File System (HDFS). Basic operations such as file listing, data reading, and summary statistics are performed to understand the structure and content of the datasets.
This experiment uses Apache Hive to run SQL queries on datasets stored in HDFS. We perform various SQL operations, such as filtering, joining, and aggregating large datasets to extract meaningful insights.
The classic MapReduce word count algorithm is implemented to count the frequency of words in a large text corpus stored in HDFS. This experiment demonstrates the Map and Reduce functions’ structure for processing large volumes of text data.
In this experiment, Apache Spark is used to analyze large datasets. You will load data into Spark Resilient Distributed Datasets (RDDs) and perform operations such as filtering, mapping, and aggregation, showcasing Spark's efficiency in big data processing.
This experiment sets up a data streaming pipeline using Apache Kafka to ingest real-time data. Apache Spark Streaming processes this data, demonstrating how real-time analytics can be performed on live data feeds.
In this experiment, Python and the Matplotlib library are used to visualize insights from large datasets. Various types of plots, such as histograms, scatter plots, and time series visualizations, are created to communicate findings effectively.
This experiment involves training machine learning models on large datasets using Apache Spark's MLlib library. Techniques such as cross-validation and model selection are utilized to evaluate and improve the performance of the models.
Using Apache Spark's GraphX library, this experiment focuses on exploring graph-structured data. Tasks include computing centrality measures, detecting communities, and performing other graph analytics tasks to uncover meaningful insights from graph data.
This experiment demonstrates data sampling techniques to create representative subsets of large datasets. Stratification methods are implemented to ensure balanced sampling based on specific criteria, which is crucial for unbiased analysis.
This experiment uses the Pandas library in Python to clean and preprocess large datasets. Issues such as missing values, outliers, and inconsistencies are addressed to prepare the data for further analysis.
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