-Key Value pairs
-Hadoop java API for Mapreduce
-Mapreduce program:Setting up classpath
-Implimenting Wordcount
-Buiding a Jar file
-Running wordcount on Local Hadoop cluster
-Running wordcount on EMR
-WordCount with combiner
-Fixing wordcount to work with combiner
-Hadoop specific data types
-Using the writable wrapper class
-Input/Output

-Using languages other than Java with Hadoop
-Word count using streaming
-Analysing large dataset
-Summarizing the UFO data
-Summarizing the shape data
-Correlating sighting duration to UFO shape
-Performing the shape/time analysis from the command line
-Using chain/mapper for field validation/analysis
-Using the distributed cache to improve location output
-Counters, status and other output, creating counters, task states and writing log output

-Simple,Adcvanced,and in-between joins
-Reduce-sidejoins using MultipleInputs
-Graph Algorithms
-Representing the graph
-Creating the source code
-The first run
-the second run
-the third run
-the fourth and the last run
-Using language -independent data structures
-Getting and installing Avro
-Defining the schema
-Creating the source Avro data with ruby
-Consumung the Avaro data with Java
-Generating the shape summaries in Mapreduce
-Examining the output data with ruby
-Examining output data with Java

-Hadoop Node Failure
-Killing a data node process
-The replication factor in action
-Intentionally causing missing blocks
-Killing a task tracker process
-Killing the job tracker
-Killing the namenode process
-Causing the task failure
-Handling dirty data by using skip mode

– EMR
– Brawsing default properties
– Setting up cluster
– Examining a default rack configuration
– Adding a rack awareness script
– Cluster access control
– Demonstrating the default security
– Managing the namenode
– Adding an additional fsimage location
– Swapping for the new namenode host
– Managing HDFS
– Mapreduce management
– Changing Job priorities and killing a job
– Scalling

– Overview of Hive
– Setting up Hive
– Installing Hive
– Creating a table for the UFO data
– Inserting the UFO data
– SAS user interface
– Validating the table
– Redefining the table with the correct column separator
– Creating a table with correct column separator
– Creating a table from an existing file
– Performing a join
– Using views
– Exporting query output
– Making a partitioned UFO sighting table
– Adding a new user defined function(UDF)
– Hive on AWS-T
– Running UFO analysis on EMR

– Common data paths
– Installing and setting up MySQL
– Configuring MySQL to allow remote connections
– Setting up the employee database
– Getting data into Hadoop
– Exporting data from MySQL to HDFS
– Exporting data from MySQL to HDFS
– Exporting data from MySQL into Hive

– AWS Services
– Getting web server data into Hadoop
– Introducing Apache flume
– Installing and configuring Flume
– Capturing network traffic to a log file
– Logging to the console
– Capturing the output of a command in a flat file
– Capturing a remote file in alocal flat file
– Writing network traffic onto HDFS
– Adding timestamps
– Multilevel flume Networks
– Writing to multiple sinks

– Hbase
– Oozie
– Pig
– R for Analytics

– Introduction
– Installation of Tableau Desktop
– Tableau architecture
– Installation of Tableau Desktop
– Tableau server component
– Tableau Environment
– Tableau Workspace
– Build views in tableau
– Connect to data source in tableau
– Export DB connection in tableau
– Data blending in tableau
– Joining tables in tableau
– Data Bins in Tableau
– Creating a dashboard in Tableau
– Tableau Desktop shortcuts Cheat Sheet.