HDP Apache Hive Training | Master Big Data Analytics with HiveQL

HDP Apache Hive

The HDP Apache Hive course is designed to teach data professionals how to manage, query, and analyze large-scale datasets in the Hadoop ecosystem using Apache Hive. Hive is a data warehouse software built on top of Hadoop that enables SQL-like queries to process and analyze big data in distributed storage. This course will guide you through the fundamentals of Hive, its architecture, and its integration with Hadoop Distributed File System (HDFS). You will also learn how to work with HiveQL (Hive Query Language) to perform complex analytics and data transformations in large datasets, making it an essential skill for data engineers, analysts, and professionals working with big data platforms.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
December 22^nd - 25^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		10% Off $1,600 $1,440 Fast Filling! Hurry Up.
December 27^th - 04^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		10% Off $1,600 $1,440
January 05^th - 08^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 10^th - 18^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 12^th - 15^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 19^th - 28^th 06:00 AM - 10:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 26^th - 29^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		20% Off $1,600 $1,280

Course Prerequisites

Basic knowledge of SQL and databases
Familiarity with the Hadoop ecosystem, including HDFS, MapReduce, and YARN
Experience with Linux/Unix commands and working in a distributed environment
A basic understanding of big data concepts is helpful but not required

Learning Objectives

By the end of this course, participants will be able to:

Understand the architecture and key features of Apache Hive
Set up and configure Hive on a Hadoop cluster and integrate it with other Hadoop components
Write HiveQL queries for analyzing large datasets in a distributed environment
Manage and optimize Hive tables using partitioning, bucketing, and indexing techniques
Query external data sources and integrate Hive with tools like Pig, HBase, and Sqoop
Optimize Hive query performance for large-scale datasets
Implement security and access control measures in Apache Hive using Kerberos and Apache Ranger
Integrate Hive with BI tools for data analysis, reporting, and visualization

Target Audience

This course is ideal for data engineers, data analysts, and professionals who work with large datasets in the Hadoop ecosystem and want to master Apache Hive for data warehousing and big data analytics. The target audience includes:

Data Engineers
Hadoop Administrators
Data Analysts
Business Intelligence (BI) Developers
Data Scientists working in big data environments
Professionals looking to work with Hadoop and Apache Hive

Course Modules

Introduction to Apache Hive
- Overview of Apache Hive and its role in the Hadoop ecosystem
- Key features of Hive and how it differs from traditional RDBMS
- Understanding Hive architecture and components (Metastore, HiveQL)
- Using Hive for data warehousing and analytics
Setting Up Hive and Hadoop Environment
- Installing and configuring Apache Hive on a Hadoop cluster
- Working with HDFS for storing data and files
- Integrating Hive with Hadoop components like HBase, Pig, and Sqoop
- Understanding the role of the Hive Metastore and managing metadata
Hive Data Types and Tables
- Understanding Hive data types and how they map to Hadoop data formats
- Creating and managing tables, partitions, and buckets in Hive
- Loading data into Hive tables from HDFS and external sources
- Querying data with HiveQL: Basic SELECT queries, filtering, and sorting
Advanced Hive Queries
- Working with joins, subqueries, and nested queries in HiveQL
- Implementing GROUP BY, ORDER BY, and HAVING clauses
- Using Hive functions for data transformations (aggregate, string, and date functions)
- Implementing complex joins and multi-table queries for big data analysis
Hive Partitioning and Bucketing
- Understanding the concepts of partitioning and bucketing in Hive
- Partitioning tables for optimized query performance
- Using bucketing for organizing large datasets in Hive
- Best practices for managing partitions and buckets in large-scale Hive queries
Working with Hive and External Data Sources
- Integrating Hive with other big data tools like Pig, HBase, and Flume
- Importing and exporting data between HDFS and external databases using Sqoop
- Querying external tables and connecting Hive with other data sources (e.g., NoSQL, Relational Databases)
- Using Hive with JSON, Parquet, and other file formats for efficient data processing
Performance Tuning and Optimization in Hive
- Techniques to optimize query performance in Hive (using indexes, caching, and partition pruning)
- MapReduce optimization and execution engine tuning for large queries
- Improving performance with Tez and Spark as Hive execution engines
- Managing large-scale datasets with Hive on Spark for faster querying
Security and Access Control in Hive
- Managing user permissions and roles in Apache Hive
- Understanding Hadoop security and integrating with Kerberos for authentication
- Using Apache Ranger for fine-grained access control and auditing
- Implementing data encryption and secure data governance in Hive environments
Hive Integration with BI Tools
- Connecting Hive to Business Intelligence (BI) tools like Tableau, Power BI, and Qlik
- Running Hive queries from BI tools for data visualization and reporting
- Best practices for integrating HiveQL with BI platforms for data analytics and decision-making

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