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COURSE SYLLABUS

MODULE 1
PL-SQL (Procedure Language – SQL)
Introduction to Programming Languages
Introduction to PL/SQL
The Advantages of PL/SQL
PL/SQL Architecture
PL/SQL Data types
During this course, you will learn:
1. Introduction to Big Data and Analytics
2. Introduction to Hadoop
3. Hadoop Ecosystem – Concepts
4. Hadoop MapReduce Concepts and Features
5. Developing the MapReduce Applications
6. Pig Concepts
7. Hive Concepts
8. Sqoop Concepts
9. Flume Concepts
10. Oozie Workflow Concepts
11. Impala Concepts
12. Hue Concepts
13. HBASE Concepts
14. ZooKeeper Concepts
15. Real Life Use Cases
Reporting Tool
1. Tableau
Virtualbox/VM Ware
1. Basics
2. Installations
3. Backups
4. Snapshots
Linux
1. Basics
2. Installations
3. Commands
Hadoop
1. Why Hadoop?
2. Scaling
3. Distributed Framework
4. Hadoop v/s RDBMS
5. Brief History of Hadoop
Setup hadoop
1. Pseudo Mode
2. Cluster Mode
3. Ipv6
4. Ssh
5. Installation of Java, Hadoop
6. Configurations of Hadoop
7. Hadoop Processes ( NN, SNN, JT, DN, TT)
8. Temporary Directory
10. Common Errors when running Hadoop Cluster, Solutions
HDFS- Hadoop Distributed File System
1. HDFS Design and Architecture
2. HDFS Concepts
3. Interacting HDFS using Command-Line
4. Interacting HDFS using Java APIs
5. Dataflow
6. Blocks
7. Replica
Hadoop Processes
1. Name Node
2. Secondary Name Node
3. Job Tracker
4. Task Tracker
5. Data Node
MapReduce
1. Developing MapReduce Application
2. Phases in MapReduce Framework
3. Map Reduce Input and Output Formats
4. Advanced Concepts
5. Sample Applications
6. Combiner
Joining datasets in MapReduce jobs
1. Map-side Join
2. Reduce-side Join
MapReduce – Customization
1. Custom Input Format Class
2. Hash Partitioner
3. Custom Partitioner
4. Sorting Techniques
5. Custom Output Format Class

Hadoop Programming Languages

I) HIVE
1. Introduction
2. Installation and Configuration
3. Interacting HDFS using HIVE
4. MapReduce Programs through HIVE
5. HIVE Commands
6. Loading, Filtering, Grouping
7. Data Types, Operators
8. Joins, Groups
9. Sample programs in HIVE
II) PIG
1. Basics
2. Installation and Configurations
3. Commands

OVERVIEW – HADOOP DEVELOPER

Introduction – The Motivation for Hadoop
1. Problems with Traditional Large-scale Systems
2. Requirements for a New Approach
Hadoop: Basic Concepts
1. An Overview of Hadoop
2. The Hadoop Distributed File System
3. Hands-On Exercise
4. How MapReduce Works
5. Hands-On Exercise
6. Anatomy of a Hadoop Cluster
7. Other Hadoop Ecosystem Components
Writing a MapReduce Program
1. The MapReduce Flow
2. Examining a Sample MapReduce Program
3. Basic MapReduce API Concepts
4. The Driver Code
5. The Mapper
6. The Reducer
7. Hadoop’s Streaming API
8. Using Eclipse for Rapid Development
9. Hands-on Exercise
10. The New MapReduce API
Common Map Reduce Algorithms
1. Sorting and Searching
2. Indexing
3. Machine Learning With Mahout
4. Term Frequency – Inverse Document Frequency
5. Word Co-occurrence
6. Hands-On Exercise
PIG Concepts..
1. Data Loading in PIG
2. Data Extraction in PIG
3. Data Transformation in PIG
4. Hands-on Exercise on PIG
Hive Concepts
1. Hive Query Language
2. Alter and Delete in Hive
3. Partition in Hive
4. Indexing
5. Joins in Hive.Unions in hive
6. Industry Specific Configuration of Hive Parameters
7. Authentication & Authorization
8. Statistics with Hive
9. Archiving in Hive
10. Hands-on exercise
Working with Sqoop
1. Introduction
2. Import Data
3. Export Data
4. Sqoop Syntax
5. Databases Connection
6. Hands-on exercise
Working with Flume
1. Introduction
2. Configuration and Setup
3. Flume Sink with Example
4. Channel
5. Flume Source with Example
6. Complex Flume Architecture
OOZIE Concepts
IMPALA Concepts
HUE Concepts
HBASE Concepts
Zookeepers concepts
Reporting Tool

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