Big Data & Hadoop | OnCampus
Big Data refers to extremely large and complex data sets that cannot be easily processed using traditional data processing applications. These data sets are often characterized by their volume, variety, velocity, and veracity, known as the 4Vs of Big Data. The challenges associated with processing and analyzing such large data sets have led to the development of specialized technologies and frameworks, one of which is Apache Hadoop.
Feature of Big Data & Hadoop
- Scalability: Big Data and Hadoop provide scalable solutions that can handle massive data sets by distributing the data and processing across multiple servers or nodes, allowing for efficient management of data growth.
- Distributed Computing: Hadoop's distributed computing model enables parallel processing of large data sets across multiple nodes, resulting in faster processing times and improved performance for data-intensive tasks.
- Fault Tolerance: Hadoop's fault-tolerant architecture ensures data reliability and availability even in the event of hardware failures, by replicating data across multiple nodes in the cluster.
- Cost-Effectiveness: Hadoop offers a cost-effective solution for storing and processing large data sets, as it can be deployed on commodity hardware, reducing the need for expensive specialized hardware.
- Flexibility: Big Data and Hadoop support various data types, including structured, semi-structured, and unstructured data, allowing for the storage and analysis of diverse data formats such as text, images, and videos.
- Data Processing Frameworks: Hadoop supports various data processing frameworks, including MapReduce, Spark, and Hive, enabling users to perform complex data processing, analysis, and querying tasks on large data sets.
- Data Security: Hadoop provides robust security features that enable data encryption, access control, and authentication, ensuring the confidentiality and integrity of sensitive data stored and processed within the Hadoop ecosystem.
- Data Storage: The Hadoop Distributed File System (HDFS) is designed to store large data sets across multiple machines, providing high availability and reliability for data storage and retrieval.
- Real-Time Data Processing: With the integration of real-time processing frameworks such as Apache Storm and Spark Streaming, Hadoop enables the processing of data in real time, allowing for immediate analysis and decision-making based on up-to-date information.
- Integration with Ecosystem Tools: Hadoop integrates with a wide range of ecosystem tools and technologies, allowing users to leverage complementary tools for data integration, data governance, data visualization, and business intelligence.
Topics in Big Data & Hadoop Workshop
- Introduction to Big Data
- Hadoop Fundamentals
- Hadoop Installation and Setup
- Hadoop MapReduce Programming
- Hadoop Distributed File System (HDFS)
- Data Ingestion and Processing in Hadoop
- Data Analysis with Hadoop
- YARN and Resource Management
- Hadoop Ecosystem Tools
- Data Security and Governance in Hadoop
- Real-Time Data Processing with Hadoop
- Data Visualization and Reporting
- Big Data Analytics Use Cases
- Performance Optimization and Monitoring
- Best Practices in Big Data and Hadoop Implementation