HDFS: Hadoop Distributed File System:

– Significance of HDFS in Hadoop
• Features of HDFS
• Five daemons of Hadoop
1. Name Node and its functionality
2. Data Node and its functionality
3. Secondary Name Node and its functionality
4. Job Tracker and its functionality
5. Task Tracker and its functionality
• Data Storage in HDFS
1. Introduction about Blocks
2. Data replication
• Accessing HDFS
1. CLI (Command Line Interface) and admin commands
2. Java-Based Approach
• Fault tolerance
• Download Hadoop
• Installation and set-up of Hadoop
1. Start-up & Shut down process
• HDFS Federation
Map Reduce:
• Map Reduce Story
• Map Reduce Architecture
• How Map Reduce works
• Developing Map Reduce
• Map Reduce Programming Model
1. Different phases of Map Reduce Algorithm.
2. Different Data types in Map Reduce.
3. how to write a basic Map Reduce Program.
• Driver Code
• 3Mapper
• Reducer
• Creating Input and Output Formats in Map Reduce Jobs
1. Text Input Format
2. Key-Value Input Format
3. Sequence File Input Format
• Data localization in Map Reduce
• Combiner (Mini Reducer) and Partitioner
• Hadoop I/O
• Distributed cache
PIG:
• Introduction to Apache Pig
• Map Reduce Vs. Apache Pig
• SQL vs. Apache Pig
• Different data types in Pig
• Modes of Execution in Pig
• Grunt shell
• Loading data
• Exploring Pig
• Latin commands
HIVE:
• Hive introduction
• Hive architecture
• Hive vs RDBMS
Dell – Internal Use – Confidential
• HiveQL and the shell
• Managing tables (external vs managed)
• Data types and schemas
• Partitions and buckets
HBASE:
• Architecture and schema design
• HBase vs. RDBMS
• HMaster and Region Servers
• Column Families and Regions
• Write pipeline
• Read pipeline
• HBase commands
Flume
SQOOP
NOTE: For Below Technologies we’ve separate content.
➔ Kafka
➔ Stream sets
➔ Scala
➔ MR
➔ Impala
➔ Spark
➔ Shell scripting/Unix