Apache Hive for Data Engineers (Hands On) with 2 Projects

Apache Hive for Data Engineers (Hands On) with 2 Projects, available at $54.99, has an average rating of 4.15, with 94 lectures, based on 36 reviews, and has 14105 subscribers.

You will learn about Why Hive is necessary for Data Engineer The goal of this course is to help you become familiar with Apache Hive bits and bytes Learn A to Z of Apache HIVE (From Basic to Advance level). Hands on Experience on Apache Hive and Real-time Use Case This course is ideal for individuals who are Software Engineer, Software Developer, Big Data Engineer, Data Engineer, Data Analyst, Data Scientist, Machine Learning Engineer or You should take this course if want to learn Apache Hive completely from scratch It is particularly useful for Software Engineer, Software Developer, Big Data Engineer, Data Engineer, Data Analyst, Data Scientist, Machine Learning Engineer or You should take this course if want to learn Apache Hive completely from scratch.

Enroll now: Apache Hive for Data Engineers (Hands On) with 2 Projects

Summary

Title: Apache Hive for Data Engineers (Hands On) with 2 Projects

Price: $54.99

Average Rating: 4.15

Number of Lectures: 94

Number of Published Lectures: 94

Number of Curriculum Items: 94

Number of Published Curriculum Objects: 94

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Why Hive is necessary for Data Engineer

The goal of this course is to help you become familiar with Apache Hive bits and bytes

Learn A to Z of Apache HIVE (From Basic to Advance level).

Hands on Experience on Apache Hive and Real-time Use Case

Who Should Attend

Software Engineer, Software Developer, Big Data Engineer, Data Engineer, Data Analyst, Data Scientist, Machine Learning Engineer

You should take this course if want to learn Apache Hive completely from scratch

Target Audiences

Software Engineer, Software Developer, Big Data Engineer, Data Engineer, Data Analyst, Data Scientist, Machine Learning Engineer

You should take this course if want to learn Apache Hive completely from scratch

The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command-line tool and JDBC driver are provided to connect users to Hive.

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Hive! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA,and more are all using Apache Hive!

Built on top of Apache Hadoop, Hive provides the following features:

Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis.

A mechanism to impose structure on a variety of data formats

Access to files stored either directly in Apache HDFS& or in other data storage systems such as Apache HBase&

Query execution via Apache Tez&, Apache Spark&, or MapReduce

Procedural language with HPL-SQL

Sub-second query retrieval via Hive LLAP, Apache YARN and Apache Slider.

Hive provides standard SQL functionality, including many of the later SQL:2003, SQL:2011, and SQL:2016 features for analytics.
Hive’s SQL can also be extended with user code via user defined functions (UDFs), user defined aggregates (UDAFs), and user defined table functions (UDTFs).

There is not a single “Hive format” in which data must be stored. Hive comes with built in connectors for comma and tab-separated values (CSV/TSV) text files, Apache Parquet&, Apache ORC&, and other formats. Users can extend Hive with connectors for other formats. Please see File Formats and Hive SerDe in the Developer Guide for details.

Hive is not designed for online transaction processing (OLTP) workloads. It is best used for traditional data warehousing tasks.

Hive is designed to maximize scalability (scale out with more machines added dynamically to the Hadoop cluster), performance, extensibility, fault-tolerance, and loose-coupling with its input formats.

We will learn

1) Apache Hive Overview

2) Apache Hive Architecture

3) Installation and Configuration

4) How a Hive query flows through the system.

5) Hive Features, Limitation and Data Model

6) Data Type, Data Definition Language, and Data Manipulation Language

7) Hive View, Partition, and Bucketing

8) Built-in Functions and Operators

9) Join in Apache Hive

10) Frequently Asked Interview Question and Answers

11) 2 Realtime Projects

My goal is to provide you with practical tools that will be beneficial for you in the future. While doing that, with a real use opportunity.

I am really excited you are here, I hope you are going to follow all the way to the end of the course. It is fairly straight forward fairly easy to follow through the course I will show you step by step each line of code & I will explain what it does and why we are doing it. So please I invite you to follow up on it to go through all the lectures. All right I will see you soon in the course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Course

Lecture 2: Introduction to Apache Hive

Lecture 3: Hive Architecture

Lecture 4: How a Hive query flows through the system.

Lecture 5: Tips to Improve Your Course Taking Experience

Lecture 6: (Optional) Introduction to Big Data

Lecture 7: (Optional) What is Hadoop

Lecture 8: Hive Features

Lecture 9: Hive Limitation

Chapter 2: Installing Apache Hive on Ubuntu (Linux) Machine

Lecture 1: Installation Steps of Hadoop

Lecture 2: Installation Steps of Apache Hive

Chapter 3: Hive Data Model

Lecture 1: Hive Data Model Diagram

Lecture 2: Tables

Lecture 3: Partitions

Lecture 4: Buckets or Clusters

Chapter 4: Hive Data Types

Lecture 1: Hive Data Types

Lecture 2: Primitive Type

Lecture 3: Complex Type

Chapter 5: HIVE Data Definition Language.

Lecture 1: Create Database

Lecture 2: Drop Database

Lecture 3: Alter Database

Lecture 4: Use Database

Lecture 5: Show Database

Lecture 6: Describe Database

Lecture 7: Create Table

Lecture 8: Create Table (Hands On)

Lecture 9: Create Table (Hands On) with all Primitive Datatype

Lecture 10: Create Table (Hands On) with all Complex Datatype

Lecture 11: Managed and External Tables

Lecture 12: Managed and External Tables (Hands On)

Lecture 13: Storage Formats

Lecture 14: Show Tables

Lecture 15: Describe Tables

Lecture 16: Drop Table

Lecture 17: Alter Table

Lecture 18: Truncate Table

Chapter 6: HIVE Data Manipulation Language

Lecture 1: LOAD

Lecture 2: SELECT

Lecture 3: INSERT

Lecture 4: UPDATE

Lecture 5: DELETE

Chapter 7: Hive Built-In Functions

Lecture 1: Date Functions

Lecture 2: Mathematical Functions

Lecture 3: String Functions

Chapter 8: Hive View, Metastore, Partitions, and Bucketing

Lecture 1: View

Lecture 2: View (Hands On)

Lecture 3: Metastore

Lecture 4: Partitions

Lecture 5: Partitions (Hands On)

Lecture 6: Bucketing

Lecture 7: Bucketing (Hands On)

Lecture 8: Hive Interactive Shell Commands

Lecture 9: Execute Hive Queries in “One Shot” Commands

Lecture 10: Executing Hive Queries from Files

Lecture 11: Hive Variable

Chapter 9: Built-in Operators

Lecture 1: Relational Operators

Lecture 2: Arithmetic Operators

Lecture 3: Logical Operators

Lecture 4: String Operators

Chapter 10: Hive Join

Lecture 1: Joins

Lecture 2: Inner Join (Hands On)

Lecture 3: Left Outer Join (Hands On)

Lecture 4: Right Outer Join (Hands On)

Lecture 5: Full Outer Join (Hands On)

Chapter 11: Working with XML and JSON

Lecture 1: Loading XML in Hive

Lecture 2: Loading JSON in Hive

Chapter 12: Frequently Asked Interview Question and Answers

Lecture 1: How to create HIVE Table with multi character delimiter?

Lecture 2: How to load Data from a .txt file to Table Stored as ORC in Hive?

Lecture 3: How to skip header rows from a table in Hive?

Lecture 4: Create single Hive table for small files without degrading performance in Hive?

Lecture 5: How will you consume this CSV file into the Hive warehouse using built SerDe?

Lecture 6: Is it possible to change the default location of a managed table?

Lecture 7: Can hive queries be executed from script files? How?

Lecture 8: Can we run unix shell commands from hive? Give example?

Chapter 13: Hands On Projects (2 Projects)

Lecture 1: Installing Apache Zeppelin (0.10.1)

Lecture 2: (Hands On) Downloading files

Lecture 3: Data Files for the Project

Lecture 4: (Hands On) Configure Hive Interpreter in Apache Zeppelin

Lecture 5: Configure Hive Interpreter in Apache Zeppelin

Lecture 6: Hadoop Configuration Setting

Lecture 7: Download Source Code for Project

Lecture 8: Starting Hadoop,Hive, Zeppelin and Uploading Source Code

Lecture 9: Project 1 – Part 1

Lecture 10: Project 1 Part 2

Lecture 11: Project 1 Part 3

Lecture 12: Project 1 Part 4

Lecture 13: Project 1 Part 5

Instructors

Bigdata Engineer
Bigdata Engineer

Rating Distribution

1 stars: 3 votes

2 stars: 1 votes

3 stars: 9 votes

4 stars: 12 votes

5 stars: 11 votes

Frequently Asked Questions

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