Real-Time Big Data
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This document discusses big data and Hadoop. It defines big data as structured and unstructured data that is analyzed for predictive purposes. Hadoop is described as an open-source software framework for distributed storage and processing of large datasets across clusters of commodity servers. Key components of Hadoop include HDFS for storage, MapReduce for processing, and YARN which allows multiple data processing engines like Spark to run on Hadoop clusters. The document also briefly outlines other big data tools that can be used with Hadoop like Flume, Sqoop, and Spark.
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- 2. What is Big Data?
- 3. Bussiness Data • Structured data • Unstructured data (not less important than structured data)
- 6. Objectives • Depend on our context • Objective first, plan later
- 7. These Three Trends • A shift to scalable, elastic computing infrastructure. • An explosion in the complexity and variety of data available. • The power and value that come from combining disparate data for comprehensive analysis.
- 8. What is Hadoop? • A file store, HDFS (Hadoop Distributed File System) • A distributed processing system: – 1.0: MapReduce – 2.0: Yarn (a distributed operating system) • Process comes to data
- 9. Hadoop 1.0 vs. Hadoop 2.0
- 10. HDFS • Designed to distributing store very large data sets reliably, and to stream those data sets at high bandwidth to distributing computation • HDFS Comics
- 12. YARN • A cluster management technology • YARN combines a central resource manager that reconciles the way applications use Hadoop system resources with node manager agents that monitor the processing operations of individual cluster nodes
- 13. YARN
- 14. Spark • Doing large scale stream processing • Achieve low latency • Comparasion: – Spark Streaming: 670k records/second/node – Storm: 115k records/second/node – Apache S4: 7.5k records/second/node
- 15. Spark • Spark offers an integrated framework for advanced analytics, including a machine learning library (MLLib), a graph engine (GraphX), a streaming analytics engine (Spark Streaming) and a fast interactive query tool (Shark)
- 16. Spark
- 17. Flume • A distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of streaming data into the Hadoop Distributed File System (HDFS) • It has a simple and flexible architecture based on streaming data flows; and is robust and fault tolerant with tunable reliability mechanisms for failover and recovery
- 18. Sqoop • A tool designed for efficiently transferring bulk data between Hadoop and structured datastores such as relational databases
- 19. RT-BigData Proposal Log Flume RDBMS Sqoop HDFS S Spark Streaming Shark GraphXMLLib Dashboards Spark Spark SQL MESOS
- 21. Images taken from: • http://www.datameer.com/images/product/big_data_ hadoop/img_bigdata.png • http://www.kdnuggets.com/websites/cartoons.html • http://www.alexjf.net/blog/distributed- systems/hadoop-yarn-installation-definitive-guide • http://hadoop.apache.org/docs/r1.2.1/hdfs_design.ht ml • http://hortonworks.com/hadoop/yarn/