Module 1: Introduction to Big Data and Hadoop Ecosystem

1. Overview of Big Data

   • Understanding Big Data and its challenges
   • Characteristics of Big Data (Volume, Variety, Velocity, Veracity)

2. Introduction to Hadoop

   • Hadoop Architecture
   • Hadoop Distributed File System (HDFS)
   • MapReduce Programming Model
   • Hadoop Ecosystem (Hive, HBase, Pig, Sqoop, etc.)

3. Why Hive?

   • Need for Hive in Big Data processing
   • SQL vs HiveQL: SQL-like querying language for Hadoop
   • Key features of Apache Hive

Module 2: Hive Architecture

1. Hive Components

   • Hive Metastore
   • Hive Driver
   • Hive Compiler
   • Execution Engine
   • HiveServer2

2. Hive Execution Flow

   • How Hive queries are executed
   • Query parsing, planning, optimization
   • Data retrieval and results

Module 3: Setting up Hive

1. Installing Hive
   • Installing Hive on Hadoop cluster
   • Installing Hive on Local Machine (Single-node setup)
   • Configuring Hive Metastore
   • Starting and stopping HiveServer2
2. Connecting Hive to Hadoop
   • Integration with HDFS
   • Setting up Hive with different storage backends (HDFS, HBase, etc.)
   • Configuring Hive with Apache Tez or Spark for optimized performance

Module 4: Introduction to HiveQL

1. Basic Data Types

   • Scalar Data Types in Hive (INT, STRING, DOUBLE, etc.)
   • Complex Data Types (ARRAY, MAP, STRUCT)

2. Creating Databases and Tables

   • Creating Databases and Tables in Hive
   • Data Types, Constraints, and Table Properties
   • External vs Managed Tables in Hive
   • Partitioning and Bucketing Tables

3. Data Loading in Hive

   • Loading data from local file system or HDFS
   • Loading data using LOAD DATA statement
   • Importing data from other data sources (e.g., relational databases, files)

Module 5: Data Manipulation in Hive

1. Basic SQL Operations

   • SELECT statement
   • Filtering data with WHERE clause
   • Sorting and Limiting Results (ORDER BY, LIMIT)
   • Aggregating Data (GROUP BY, COUNT, SUM, AVG)

2. Join Operations

   • INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
   • Working with Complex Joins in Hive

3. Inserting, Updating, and Deleting Data

   • Inserting data into tables
   • Updating and Deleting data in Hive

Module 6: Advanced HiveQL Features

1. Subqueries
   • Using Subqueries in SELECT, FROM, and WHERE clauses
2. User Defined Functions (UDFs)
   • Introduction to UDFs in Hive
   • Writing and registering custom UDFs in Java or Python
3. Window Functions
   • Working with Window Functions (ROW_NUMBER, RANK, etc.)
4. Optimizing Queries
   • Using EXPLAIN to view query execution plans
   • Performance Tuning with Partitioning, Bucketing, and Indexing

Module 7: Partitioning and Bucketing in Hive

1. Partitioning in Hive

   • What is Partitioning?
   • Creating and managing Partitioned Tables
   • Querying Partitioned Tables

2. Bucketing in Hive

   • What is Bucketing?
   • Creating and managing Bucketed Tables
   • Differences between Partitioning and Bucketing

3. Dynamic Partitioning

   • How to insert data into dynamically partitioned tables
   • Performance considerations for Partitioning and Bucketing

Module 8: Advanced Data Processing

1. Working with Large Datasets

   • Optimizing queries for big datasets
   • Techniques for reducing I/O (file formats like ORC, Parquet)

2. Hive with Apache Tez and Apache Spark

   • Introduction to Tez and Spark execution engines
   • Benefits of using Tez or Spark with Hive

3. Compression Techniques

   • Data compression formats: Gzip, Snappy, LZO, ORC, Parquet
   • Understanding the trade-offs between compression and performance

Module 9: Integration with Hadoop Ecosystem

1. Hive and HDFS

   • How Hive integrates with Hadoop Distributed File System (HDFS)
   • Data Loading and Storage in HDFS

2. Hive and HBase

   • Storing Hive data in HBase for real-time access
   • Reading and writing HBase data using Hive

3. Hive and Pig

   • Using Apache Pig for data transformation
   • Integrating Pig scripts with Hive queries

Module 10: Security and Permissions in Hive

1. Hive Authentication and Authorization

   • Configuring Kerberos for authentication
   • Implementing Role-based Access Control (RBAC) in Hive

2. Data Encryption and Auditing

   • Data Encryption using Hive
   • Configuring Hive Audit Logs for security

3. Managing Permissions

   • Granting and revoking privileges on databases, tables, and columns