Fiber distributed data interface

mso-bidi-theme-font:minor-latin">Apache Spark has become one of the key cluster-computing frame works in the world. Spark can be deployed in numereous ways like in machine Learning, Streaming data and graphic processing. Spark supports programming languages like Python, Scala, Java, and R. Apache Hadoop mso-bidi-theme-font:minor-latin"> is an open-source framework written in Java that allows us to store and process Big Data in a distributed environment, across various clusters of computers using simple programming constructs. To do this, Hadoop uses an algorithm called  Map Reduce mso-bidi-theme-font:minor-latin">, which divides the task into small parts and assigns them to a set of computers. Hadoop also has its own file system,  Hadoop Distributed File System (HDFS),  which is based on  Google File System (GFS). HDFS is designed to run on low-cost hardware. Apache Spark mso-bidi-theme-font:minor-latin"> is an open-source distributed cluster-computing framework. Spark is a data processing engine developed to provide faster and easy-to-use analytics than  Hadoop Ma pReduce. mso-bidi-theme-font:minor-latin;color:#222222;mso-ansi-language:EN-GB">Apache Spark in the big data industry is because of its in-memory data processing that makes it high-speed data processing engine compare to Map Reduce. Apache Spark has huge potential to contribute to Big data related business in the industry.  Apache Spark is a Big data processing interface which provides not only programming interface in the data cluster but also adequate fault tolerance and data parallelism. This open-source platform is efficient in speedy processing of massive datasets. Calibri;mso-bidi-theme-font:minor-latin;color:#222222"> 115%;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin">Contact us: mso-bidi-theme-font:minor-latin">  http://www.monstercourses.com/ mso-bidi-theme-font:minor-latin">USA:  + 1 772 777 1557 line-height:115%;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin; color:red"> & +44 702 409 4077 line-height:115%;mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin"> mso-bidi-font-family:Calibri;mso-bidi-theme-font:minor-latin">Skype ID: MonsterCourses Calibri;mso-bidi-theme-font:minor-latin"> mso-bidi-theme-font:minor-latin"> 

Hadoop for Developers and Administrators Hadoop for Developers and Administrators Syllabus COURSE CONTENT Traning at hadoop school of training Magarpatta city Pune. (+91-93257-93756) www.hadoopschooloftraining.co.in Next level of development and administration Schedule: Day 1:BigData • Why is Big Data important • What is Big Data • Characteristics of Big Data • How did data become so big • Why should you care about Big Data • Use Cases of Big Data Analysis • What are possible options for analyzing big data • Traditional Distributed Systems • Problems with traditional distributed systems • What is Hadoop • History of Hadoop • How does Hadoop solve Big Data problem • Components of Hadoop • What is HDFS • How HDFS works • What is Mapreduce • How Mapreduce works • How Hadoop works as a system Day2: Hadoop ecosystem • Pig • What is Pig • How it works • An example • Hive • What is Hive • How it works • An example • Flume • What is Flume • How it works • An example • Sqoop • What is Sqoop • How it works • An example • Oozie • What is Oozie • How it works • An example • HDFS in detail • Map Reduce in details Day3: Hands On-¬‐ • VMsetup • Setting up Virtual Machine • Installing Hadoop in Pseudo Distributed mode Day 4: Hands On-¬‐ • Programs • Running your first MapReduce Program • Sqoop Hands on • Flume Hands Day 5: • Multinode cluster setup • Setting up a multimode cluster Day 6: • Planning your Hadoop cluster • Considerations for setting up Hadoop Cluster • Hardware considerations • Software considerations • Other considerations Day 7: Disecting the Wordcount Program • Understanding the Driver • Understanding the Mapper • Understanding the Reducer Day 8: • Diving deeper into MapReduce • API • Understanding combiners • Understanding partitioners • Understanding input formats • Understanding output formats • Distributed Cache • Understanding Counters Day 9: • Common Mapreduce patterns • Sorting Serching • Inverted Indexes Day 10: • Common Mapreduce patterns • TF-IDF • Word-Cooccurance Day 11: • Hands on Mapreduce Day 12: • Hands on Mapreduce Day 13: • Introduction to Pig and Hive • Pig program structure and execution process • Joins • Filtering • Group and Co-Group • Schema merging and redefining schema • Pig functions • Motivation and Understanding Hive • Using Hive Command line Interface • Data types and File Formats • Basic DDL operations • Schema Design Day 14: • Hands on Hive and Pig Day 15: • Advanced Hadoop Concepts • Yarn • Hadoop Federation • Authntication in Hadoop • High Availbability Day 16: • Administration Refresher • Setting up hadoop cluster - Considerations • Most important configurations • Installation options Day 17: • Scheduling in Hadoop • FIFO Scheduler • Fair Scheduler Day 18: • Monitoring your Hadoop Cluster • Monitoring tools available • Ganglia • Monitoring best practices Day 19: • Administration Best practices • Hadoop Administration best practices • Tools of the trade Day 20: • Test • Test – 50 questions test (20- Development releated, 20- • Administration related and 10 Hadoop in General Please Contact- Hadoop School of Training Destination Center, Second Floor Magarpatta City Pune: 411013 Phone: India: +91-93257-93756 USA: 001-347-983-8512 www.hadoopschooloftraining.co.in Email: learninghub01@gmail.com Skype: learning.hub01