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:
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Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis.
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A mechanism to impose structure on a variety of data formats
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Access to files stored either directly in Apache HDFS™ or in other data storage systems such as Apache HBase™
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Query execution via Apache Tez™, Apache Spark™, or MapReduce
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Procedural language with HPL-SQL
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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
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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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