Easy Learning Hadoop MapReduce and BigData Video Training Tutorial DVD(DVD)

Description

SECTION 1: Introduction to Big Data 1 Introduction to the Course 04:55 Introduction to the Course 2 Why Hadoop, Big Data and Map Reduce Part – A 10:44 Introduction to Big Data, Hadoop and Map Reduce 3 Why Hadoop, Big Data and Map Reduce Part – B 10:22 4 Why Hadoop, Big Data and Map Reduce Part – C 12:34 5 Architecture of Clusters 19:09 Lecture to help you understand the server cluster architecture 6 Virtual Machine VM, Provisioning a VM with vagrant and puppet 15:54 Learn all about virtual machine provisioning SECTION 2: Hadoop Architecture 7 Set up a single Node Hadoop pseudo cluster Part – A 11:08 Learn to setup the single node cluster 8 Set up a single Node Hadoop pseudo cluster Part – B 12:34 9 Set up a single Node Hadoop pseudo cluster Part – c 12:07 10 Clusters and Nodes, Hadoop Cluster Part – A 13:23 Learn to set up a Hadoop Cluster 11 Clusters and Nodes, Hadoop Cluster Part – B 14:15 12 NameNode, Secondary Name Node, Data Nodes Part – A 11:05 Lecture about Node Hiearchy 13 NameNode, Secondary Name Node, Data Nodes Part – B 10:14 14 Running Multi node clusters SECTION 3: Distributed file systems 19 Hdfs vs Gfs a comparison – Part A 13:17 A comparison between HDFS and GFS file systems 20 Hdfs vs Gfs a comparison – Part B 06:12 21 Run hadoop on Cloudera, Web Administration 17:32 Learn to Run Hadoop on Cloudera 22 Run hadoop on Hortonworks Sandbox 19:14 Learn to run Hadoop on Hortonworks 23 File system operations with the HDFS shell Part – A 14:03 Learn to perform file system operations using HDFS Shell 24 File system operations with the HDFS shell Part – B 19:37 25 Advanced hadoop development with Apache Bigtop Part – A 13:12 Learn all about Hadoop development using Apache Bigtop 26 Advanced hadoop development with Apache Bigtop Part – B 07:10 SECTION 4: Mapreduce Version 1 27 MapReduce Concepts in detail Part – A 13:12 Learn the underlying concepts of the Map Reduce algorithm 28 MapReduce Concepts in detail Part – B 10:55 29 Jobs definition, Job configuration, submission, execution and monitoring Part -A 09:39 Learn to create Hadoop Jobs 30 Jobs definition, Job configuration, submission, execution and monitoring Part -B 10:44 31 Jobs definition, Job configuration, submission, execution and monitoring Part -C 16:48 32 Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part A 09:32 Learn the basic syntax of Hadoop 33 Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part B 10:39 34 Hadoop Data Types, Paths, FileSystem, Splitters, Readers and Writers Part C 18:52 35 The ETL class, Definition, Extract, Transform, and Load Part – A 15:14 Learn all about the ETL class definition, transformation and load 36 The ETL class, Definition, Extract, Transform, and Load Part – B 24:14 37 The UDF class, Definition, User Defined Functions Part – A 12:18 Learn the basics of User defined class and functions 38 The UDF class, Definition, User Defined Functions Part – B 13:01 SECTION 5: Mapreduce with Hive Data warehousing 39 Schema design for a Data warehouse Part – A 13:28 Learn the schema design for data warehousing 40 Schema design for a Data warehouse Part – B 12:00 41 Hive Configuration 17:53 Introduction to Hive and its use for Data Warehousing 42 Hive Query Patterns Part – A 13:52 Learn all about Hive Query Patterns 43 Hive Query Patterns Part – B 12:54 44 Hive Query Patterns Part – C 16:42 45 Example Hive ETL class Part – A 14:02 A live example to implement Hive ETL class 46 Example Hive ETL class Part – B 11:11 SECTION 6: Mapreduce with Pig Parallel processing 47 Introduction to Apache Pig Part – A 12:17 Introduction to Parallel Processing using Apache Pig 48 Introduction to Apache Pig Part – B 13:45 49 Introduction to Apache Pig Part – C 09:07 50 Introduction to Apache Pig Part – D 10:09 51 Pig LoadFunc and EvalFunc classes 13:28 Advance Pig features and usage of LoadFunc and EvalFunc Class 52 Example Pig ETL class Part – A 12:40 A working example of PIG ETL class 53 Example Pig ETL class Part – B 14:11