Big data and hadoop ecosystem tools
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This document provides an introduction to big data and Hadoop. It discusses the volume, velocity, and variety properties of big data. It then explains the core components of Hadoop including the NameNode, DataNodes, and HDFS. It also summarizes MapReduce and key Hadoop ecosystem tools like Hive, Pig, Oozie, and HBase. It concludes that Hadoop is an open source tool that can handle large, diverse data sets and provide fault tolerance through data replication across nodes.
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Hadoop
Big data refers to large volumes of data that are diverse in type and are produced rapidly. It is characterized by the V's: volume, velocity, variety, veracity, and value. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of commodity servers. It has two main components: HDFS for storage and MapReduce for processing. Hadoop allows for the distributed processing of large data sets across clusters in a reliable, fault-tolerant manner. The Hadoop ecosystem includes additional tools like HBase, Hive, Pig and Zookeeper that help access and manage data. Understanding Hadoop is a valuable skill as many companies now rely on big data and Hadoop technologies.
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Big data and hadoop ecosystem tools
- 2. Introduction to Big Data Properties of Big Data Introduction to Hadoop Core components in Hadoop MapReduce Hadoop Ecosystem tools Conclusion
- 3. A data which is beyond storage capacity and beyond processing power Properties of Big Data According to IBM Volume Velocity variety
- 5. 1. Structured Data RDBMS 2. Semi Structured Data Log Files 3. Unstructured Data text, audio, video, image etc..
- 7. ? ?
- 8. 4.545
- 11. Name Node Master of the system Maintains and manages the blocks of data nodes Data Node salves and provides actual storage responsible for read and write operations
- 13. Highly fault-tolerant High Throughput Suitable for applications with large data dets Write once and read many times Can be built by commodity hardware Replicating data across different data nodes
- 14. Low latency data access(quickly access small data) Lots of small files Multiple writes, arbitrary file modifications
- 17. Familiar with SQL use Initially given by Facebook Internally runs with MapReduce HiveQL-Hive Query Language act as interpreter Can load thousands of rows at a time
- 18. Importing data from RDBMS to HDFS Exporting data from HDFS to RBMS Used to Store data in Hbase Used to upload data to Hive
- 19. No need of lot of knowledge in programming and SQL Simplifies the work done by mapreduce programs Initially given by Yahoo Own language “Pig Latin Scripting”
- 20. Works as a server Coordinating more than one job at a time
- 21. No SQL Column Oriented Format Data can be stored and processed
- 22. Hadoop can handle any type of data Open Source from Apache Fault Tolerant Provides tools for various domain knowledge Works very fast compared to others