Hadoop Online Training in Noida, India
Ambient Info Solutions offering the Hadoop Online Training in Noida, India under the guidance of real time working experts.
1. Introduction
1.1 Big Data Introduction
1-What is Big Data
2-Data Analytics
3-Bigdata Challenges
4-Technologies supported by big data
1.2 Hadoop Introduction
1-What is Hadoop?
2-History of Hadoop
3-Basic Concepts
4-Future of Hadoop
5-The Hadoop Distributed File System
6-Anatomy of a Hadoop Cluster
7-Breakthroughs of Hadoop
8-Hadoop Distributions:
9-Apache Hadoop
10-Cloudera Hadoop
11-Horton Networks Hadoop
12-MapR Hadoop
2. Hadoop Daemon Processes
1-Name Node
2-DataNode
3-Secondary Name Node
4-Job Tracker
5-Task Tracker
3. HDFS (Hadoop Distributed File System)
1-Blocks and Input Splits
2-Data Replication
3-Hadoop Rack Awareness
4-Cluster Architecture and Block Placement
5-Accessing HDFS
6-JAVA Approach
7-CLI Approach
4. Hadoop Installation Modes and HDFS
1-Local Mode
2-Pseudo-distributed Mode
3-Fully distributed mode
4-Pseudo Mode installation and configurations
5-HDFS basic file operations
5. Hadoop Developer Tasks
1-5.1 Writing a MapReduce Program
2-Basic API Concepts
3-The Driver Class
4-The Mapper Class
5-The Reducer Class
6-The Combiner Class
7-The Partitioner Class
8-Examining a Sample MapReduce Program with several examples
9-Hadoop’s Streaming API
10-Examining a Sample MapReduce Program with several examples
11-Running your MapReduce program on Hadoop 1.0
12-Running your MapReduce Program on Hadoop 2.0
5.2 Performing several hadoop jobs
1-Sequence Files
2-Record Reader
3-Record Writer
4-Role of Reporter
5-Output Collector
6-Processing XML files
7-Counters
8-Directly Accessing HDFS
9-ToolRunner
10-Using The Distributed Cache
5.3 Advanced MapReduce Programming
1-A Recap of the MapReduce Flow
2-The Secondary Sort
3-Customized Input Formats and Output Formats
4-Map-Side Joins
5-Reduce-Side Joins
5.4 Practical Development Tips and Techniques
1-Strategies for Debugging MapReduce Code
2-Testing MapReduce Code Locally by Using LocalJobRunner
3-Testing with MRUnit
4-Writing and Viewing Log Files
5-Retrieving Job Information with Counters
6-Reusing Objects
5.5 Data Input and Output
1- Creating Custom Writable and Writable-Comparable Implementations
2- Saving Binary Data Using SequenceFile and Avro Data Files
3-Issues to Consider When Using File Compression
5.6 Tuning for Performance in MapReduce
1-Reducing network traffic with Combiner, Partitioner classes
2-Reducing the amount of input data using compression
3-Reusing the JVM
4-Running with speculative execution
5-Input Formatters
6-Output Formatters
7-Schedulers
8-FIFO schedulers
9-FAIR Schedulers
10-CAPACITY Schedulers
5.7 YARN
1-What is YARN
2-How YARN Works
3-Advantages of YARN
6. Hadoop Ecosystems
6.1 PIG
1-PIG concepts
2-Install and configure PIG on a cluster
3-PIG Vs MapReduce and SQL
4-PIG Vs HIVE
5-Write sample PIG Latin scripts
6-Modes of running PIG
7-Programming in Eclipse
8-Running as Java program
9-PIG UDFs
10-PIG Macros
6.2 HIVE
1-Hive concepts
2-Hive architecture
3-Installing and configuring HIVE
4-Managed tables and external tables
5-Partitioned tables
6-Bucketed tables
7-Complex data types
8-Joins in HIVE
9-Multiple ways of inserting data in HIVE tables
10-CTAS, views, alter tables
11-User defined functions in HIVE
12-Hive UDF
13-Hive UDAF
14-Hive UDTF
6.3 SQOOP
1-SQOOP concepts
2-SQOOP architecture
3-Install and configure SQOOP
4-Connecting to RDBMS
5-Internal mechanism of import/export
6-Import data from Oracle/Mysql to HIVE
7-Export data to Oracle/Mysql
8-Other SQOOP commands
9-6.4 HBASE
10-HBASE concepts
11-ZOOKEEPER concepts
12-HBASE and Region server architecture
13-File storage architecture
14-NoSQL vs SQL
15-Defining Schema and basic operations
16-DDLs
17-DMLs
18-HBASE use cases
19-Access data stored in HBASE using clients like CLI, and Java
20-Map Reduce client to access the HBASE data
21-HBASE admin tasks
6.5 OOZIE
1-OOZIE concepts
2-OOZIE architecture
3-Workflow engine
4-Job coordinator
5-Install and configuring OOZIE
6-HPDL and XML for creating Workflows
7-Nodes in OOZIE
8-Action nodes
9-Control nodes
10-Accessing OOZIE jobs through CLI, and web console
11-Develop sample workflows in OOZIE on various Hadoop distributions
12-Run HDFS file operations
13-Run MapReduce programs
14-Run PIG scripts
15-Run HIVE jobs
16-Run SQOOP Imports/Exports
6.6 FLUME
1-FLUME Concepts
2-FLUME architecture
3-Installation and configurations
4-Executing FLUME jobs
6.7 IMPALA
1-What is Impala
2-How Impala Works
3-Imapla Vs Hive
4-Impala’s shortcomings
5-Impala Hands on
6.8 ZOOKEEPER
1-ZOOKEEPER Concepts
2-Zookeeper as a service
3-Zookeeper in production
7. Integrations
1-Mapreduce and HIVE integrationv
2-Mapreduce and HBASE integration
3-Java and HIVE integration
4-HIVE – HBASE Integration
5-SAS – HADOOP
8. Spark
1-Introduction to Scala
2-Functional Programming in Scala
3-Working with RDDs – Spark
. Hadoop
1-Administrative Tasks:
2-Setup Hadoop cluster: Apache, Cloudera and VMware
3-Install and configure Apache Hadoop on a multi node cluster
4-Install and configure Cloudera Hadoop distribution in fully distributed mode
5-Install and configure different ecosystems
6-Basic Administrative tasks
10. Course Deliverables
1-Workshop style coaching
2-Interactive approach
3-Course material
4-Hands on practice exercises for each topic
5-Quiz at the end of each major topic
6-Tips and techniques on Cloudera Certification Examination
7-Linux concepts and basic commands
8-On Demand Services
9-Mock interviews for each individual will be conducted on need basis
10-SQL basics on need basis
11-Core Java concepts on need basis
12-Resume preparation and guidance
13-Interview questions
14-Duration:60 Hrs
15-Class Duration:1 Hr 30 Mins
16-Faculty Name: Nagaraju
17-Experience : 10 Yrs in IT 5+ Yrs in Hadoop