• Core Java
• Database concepts of SQL
• Hadoop Filesystem, and
• any of Linux operating system flavors

Access Hadoop Data using Hive Course Content

Lesson 1: Introduction

Hive helps to query and manage large datasets. Hive architecture consists of three core components namely Hive Clients, Hive Services and Hive Storage and Computing

Class 1.1:

• Overview of Hive
• Uses of Hive
• Compare Hive with other technologies

Class 1.2:

• Overview of Hive Architecture
• About Hive Components
• Usage of Hive by other industries

Lesson 2: Hive DDL

Hive consists of primitive data types and collection data types like arrays and maps to operate the data tables.

Class 2.1:

• Methods to Create Database and Tables
• Usage of different data types
• Run DDL commands

Class 2.2:

• Improve performance of Hive queries using Partitioning
• Create Hive managed and external tables

Lesson 3: Hive DML

Hive supports CLI to write Hive queries using Hive Query Language (HQL). HQL syntax is similar to the SQL syntax. Hive reuses concepts like tables, rows, columns and schema from the relational database.

Class 3.1:

• Loading data in Hive
• Exporting data out of Hive
• Running Hive QL DML queries

Lesson 4: Hive Operators and Functions

This course teaches you to use built-in operators and functions that help in implementing Data operations on the data tables inside Hive.

Hive supports many built-in functions divided into categories that include mathematical and statistical functions, string functions, conditional functions and date functions (for operating on string representations of dates).

Class 4.1:

• Using Hive Operators in your queries
• Utilize Built-in Functions of Hive
• Extending Hive functionality

Lesson 5: Data Extraction using Hive

• Use Hive to work with Structured Data
• Use Hive to work with Semi-structured data (XML, JSON)
• Hive in Real time projects

Access Hadoop Data using Hive FAQs

1. What is Hive?

Hive is a Data warehousing tool designed on top of Hadoop Distributed File System (HDFS). Hive makes job easy for performing operations like Data encapsulation, querying and analysis of massive datasets

2. What are the key points of Hive?

Some of the key points are:

• The difference between HQL and SQL is that Hive query executes on Hadoop's infrastructure more than the traditional database
• The Hive query execution is more like series of automatically generated map reduce Jobs
• Once client executes the query, Hive supports partition and buckets concepts for easy retrieval of data
• Data cleansing and filtering is performed using custom specific UDF (User Defined Functions)

3. What are the features of Hive?

• Hive stores schema in a database and processed data into HDFS
• Hive supports OLAP
• Hive provides SQL-type language for querying called HiveQL or HQL
• Hive is familiar, fast, scalable, and extensible

4. What is the difference between traditional databases and Hive?

The fundamental differences between Hive and relational databases are as follows:

Relational databases allow creating a table then insert data into the table. On relational database tables, you can perform functions like Insertions, Updates, and Modifications.

Hive means functions like the update and modifications do not work and hence you will not be able to update and modify data across multiple nodes. Hive supports read many and write once pattern which means that after inserting table you will be able to update the table in the latest Hive versions.

5. What are the different modes of Hive?

Hive operates in three modes based on the size of data nodes in Hadoop. These modes are the Local mode, Mapreduce mode, and Pseudo distributed mode.

6. What is meant by metastore in Hive?

Metastore is a relational database for storing the metadata of hive tables, partitions, Hive databases.