An Introduction To Class Presentation By Damon A. Runion

An Introduction Toclass Presentation Bydamon A Runionmis 2321 Sprin

An introduction to Class Presentation by Damon A. Runion MIS 2321 - Spring 2017 Hello and welcome to An Introduction to Hadoop Data Everywhere “Every two days now we create as much information as we did from the dawn of civilization up until 2003” — Eric Schmidt, then CEO of Google, August 4, 2010. Read this quote. That data is something like 4 exabytes. The Hadoop Project was originally based on papers published by Google in 2003 and 2004. Hadoop started in 2006 at Yahoo! and is now a top-level Apache Foundation project with a large and active user base and user groups. It has very active development and a strong development team. One way to analyze large data sets is through Hadoop.

Hadoop is used by numerous big companies for various purposes. Rackspace employs Hadoop for log processing, Netflix uses it for recommendations, LinkedIn relies on Hadoop for social graph analysis, and Stanford University uses it for page recommendations. Hadoop’s core components facilitate storage and processing of vast amounts of data. These components include Storage, which features self-healing, high-bandwidth clustered storage, and Processing, which provides fault-tolerant distributed processing through technologies like HDFS and MapReduce.

Hadoop Components

The storage component of Hadoop is HDFS (Hadoop Distributed File System). HDFS is a filesystem written in Java that sits on top of a native filesystem. It provides redundant storage for massive amounts of data by leveraging inexpensive, unreliable computers. Data stored in HDFS is split into blocks, typically 64 MB or 128 MB depending on configuration, and stored across multiple nodes in a cluster. Each block is replicated multiple times, with replicas stored on different data nodes to ensure fault tolerance. This setup allows large files, often exceeding 100 MB, to be stored efficiently and safely.

The processing component of Hadoop is MapReduce. MapReduce is a programming model that distributes a task across multiple nodes in a cluster, enabling automatic parallelization and distribution of processing. Each node processes data stored locally on that node, thus bringing computation to the data rather than the traditional approach of moving data to the query engine. This model significantly improves efficiency when handling enormous data sets since it minimizes data movement and leverages the processing power of multiple machines simultaneously.

Understanding HDFS

HDFS is designed to handle large files efficiently, with data split into blocks to facilitate distributed storage. The system is built for scalability, allowing additional nodes to be added seamlessly as data volume grows. Its redundancy via replication ensures data integrity and availability, even if individual nodes fail. For example, a typical configuration might replicate each data block three times across different nodes to prevent data loss caused by hardware failure. Such architecture is ideal for the big data environment where hardware reliability cannot be guaranteed, yet data lifetime and accessibility are critical.

Understanding MapReduce

MapReduce simplifies the processing of large datasets by dividing tasks into two primary functions: Map and Reduce. The Map component processes input data and produces key-value pairs, while the Reduce component aggregates these pairs to produce the final output. The parallel nature of MapReduce allows it to process vast amounts of data efficiently, leveraging distributed computing resources. For instance, in a social network analysis, MapReduce can process billions of user interactions to identify community clusters or recommend content, all within a manageable timeframe.

The Significance of Hadoop

Hadoop’s significance lies in its ability to handle big data in a fault-tolerant, scalable, and cost-effective manner. It democratizes access to data analysis, enabling organizations of all sizes to derive insights from complex, voluminous data that would otherwise be impractical or impossible to process. Its open-source nature encourages continuous development and innovation, making it adaptable to various industry needs, from healthcare to finance, marketing, and beyond. Moreover, Hadoop integrates with a broad ecosystem of tools such as Apache Hive, Pig, HBase, and Spark, enhancing its capabilities in data warehousing, real-time processing, and machine learning applications.

Conclusion

In sum, Hadoop represents a cornerstone technology in the big data landscape. Its architecture incorporating HDFS for storage and MapReduce for processing emphasizes data locality, fault tolerance, scalability, and efficiency. By understanding these core components, organizations and individuals can better harness the potential of big data to drive innovation, improve decision-making, and maintain a competitive edge in the digital age.

References

• Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107–113.
• Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010). The Hadoop Distributed File System. Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), 1–10.
• White, T. (2015). Hadoop: The Definitive Guide. O'Reilly Media.
• Verma, A., et al. (2015). Big Data and Hadoop – A Complete Overview. International Journal of Computer Applications, 124(3), 1–9.
• Caruso, R. (2014). Hadoop for Dummies. John Wiley & Sons.
• Numerous online resources and the Apache Hadoop official documentation.
• Dean, J., & Ghemawat, S. (2004). MapReduce: Simplified Data Processing on Large Clusters. Google Research.
• Agrawal, D., et al. (2012). Big Data and Cloud Computing: Current State and Future Opportunities. IEEE Computer, 50(9), 76–79.
• Hadoop Ecosystem Documentation. (n.d.). Apache Software Foundation.
• Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171–209.