HDP Apache Hive Course Overview

HDP Apache Hive Course Overview

The HDP Apache Hive course is a comprehensive program designed to equip learners with in-depth knowledge of Apache Hive, a data warehouse software project that facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Through an array of modules, the course offers a blend of theoretical understanding and practical skills, from the basics of information architecture in Module 1 to advanced performance tuning in Module 8.

Learners will explore the Apache Hive Architecture, understand various file formats, and delve into advanced programming techniques. The course also covers integration with other big data components like Apache HBase, Phoenix, Druid, Sqoop, and Spark, ensuring a holistic grasp of the data ecosystem. Modules on security with Apache Ranger and Atlas, as well as performance enhancement with LLAP, are also included.

By the end of the course, participants will be well-versed in Apache Hive, capable of optimizing enterprise data warehouses, and will have gained practical exposure to Hive's integration with various tools, enhancing their big data skill set and opening up opportunities in data engineering and analytics.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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  • Live Online Training (Duration : 32 Hours)
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♱ Excluding VAT/GST

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Course Prerequisites

To ensure a productive and successful learning experience in the HDP Apache Hive course, participants should have the following minimum prerequisites:


  • Basic understanding of SQL: Familiarity with SQL queries and relational databases is essential as HiveQL (Hive Query Language) is similar to SQL.
  • Fundamental knowledge of Linux: Basic command-line operations in a Linux environment will be beneficial as Apache Hive and related components often require interaction with Linux systems.
  • Awareness of Big Data concepts: An introductory level understanding of Big Data and its challenges will help in comprehending the use cases and importance of Apache Hive in this domain.
  • Some experience with data warehousing concepts: While not mandatory, knowledge of data warehousing principles can be advantageous for grasping Enterprise Data Warehouse optimization topics.
  • Familiarity with Hadoop ecosystem: Knowledge of the Hadoop framework and its core components (HDFS, MapReduce) is helpful, as Hive is a part of the Hadoop ecosystem.

Note that while these are the minimum prerequisites, the course is designed to accommodate learners with various levels of expertise. Prior exposure to the mentioned areas will facilitate a smoother learning curve, but the course content will provide comprehensive coverage of the necessary topics.


Target Audience for HDP Apache Hive

The HDP Apache Hive course is tailored for IT professionals aiming to master data warehousing and query optimization using Hive.


  • Data Engineers
  • Big Data Analysts
  • Database Administrators
  • Business Intelligence Professionals
  • Data Scientists
  • IT Developers with a focus on Big Data solutions
  • Software Engineers looking to specialize in Big Data technologies
  • System Architects designing Big Data solutions
  • Technical Project Managers overseeing Big Data projects
  • Professionals seeking to optimize enterprise data warehouse performance
  • Data Management Professionals
  • Data Governance and Security Analysts
  • IT Consultants working on Big Data platforms


Learning Objectives - What you will Learn in this HDP Apache Hive?

Brief Introduction to Course Learning Outcomes:

Gain expertise in Apache Hive with a comprehensive course covering optimization, architecture, programming, performance tuning, security, data governance, integration with Hadoop ecosystem components, and real-time processing with LLAP.

Learning Objectives and Outcomes:

  • Understand the role of Apache Hive in optimizing the Enterprise Data Warehouse and managing Big Data.
  • Learn the fundamentals of Apache Hive, including its interface with tools like Apache Zeppelin and Apache Superset.
  • Grasp the architectural components of Apache Hive and how it processes large datasets.
  • Develop skills in writing Hive queries and managing data with Hive ACID transactions.
  • Explore different file formats and SerDes, and their implications on data storage and retrieval in Hive.
  • Implement data organization techniques using partitions, bucketing, and handling data skew.
  • Master advanced Hive programming concepts including UDFs, subqueries, views, joins, and windowing functions.
  • Optimize Hive queries with cost-based optimization, statistics, and understand execution plans for efficient resource utilization.
  • Deep dive into LLAP for real-time query processing and learn about its configuration and performance aspects.
  • Address security and governance in Hive with tools like Apache Ranger and Apache Atlas, and understand integration with HBase, Druid, Sqoop, Spark, and NiFi.