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Mastering Hive: From Basics to Advanced Big Data Analysis

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Mastering Hive: From Basics to Advanced Big Data Analysis, available at $54.99, has an average rating of 4.5, with 190 lectures, based on 1 reviews, and has 424 subscribers.

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You will learn about Introduction to Hive: Understand the fundamentals of Hive and its role in the Hadoop ecosystem. Hive Database Management: Learn how to create and manage Hive databases and tables. Data Loading and Manipulation: Master the techniques for loading data into Hive and performing data manipulation operations. Advanced Querying: Execute complex queries using HiveQL, including joins, partitions, and bucketing. Hive Functions: Utilize built-in Hive functions for data processing and analysis. User Defined Functions (UDFs): Create and implement custom UDFs to extend Hive's capabilities. Hive Integration with HBase: Explore the integration of Hive with HBase for efficient data storage and retrieval. Real-World Case Studies: Apply Hive knowledge to practical case studies in various industries, such as telecom and social media. Hive with Other Big Data Tools: Learn to use Hive in conjunction with Pig, MapReduce, and Sqoop for comprehensive data analysis. Sensor Data Analysis: Gain hands-on experience in processing and analyzing sensor data using Hive and Pig. This course is ideal for individuals who are Aspiring Data Engineers: Individuals aiming to build a career in data engineering and big data analytics. or Big Data Enthusiasts: Anyone with a passion for big data technologies and analytics. or Data Analysts: Professionals seeking to enhance their data analysis skills with Hive. or Students: Computer science and engineering students interested in learning about big data technologies. or IT Professionals: IT professionals looking to upskill and transition into big data roles. or Software Developers: Developers wanting to integrate Hive capabilities into their applications. or Tech Entrepreneurs: Entrepreneurs looking to implement big data solutions in their startups. It is particularly useful for Aspiring Data Engineers: Individuals aiming to build a career in data engineering and big data analytics. or Big Data Enthusiasts: Anyone with a passion for big data technologies and analytics. or Data Analysts: Professionals seeking to enhance their data analysis skills with Hive. or Students: Computer science and engineering students interested in learning about big data technologies. or IT Professionals: IT professionals looking to upskill and transition into big data roles. or Software Developers: Developers wanting to integrate Hive capabilities into their applications. or Tech Entrepreneurs: Entrepreneurs looking to implement big data solutions in their startups.

Enroll now: Mastering Hive: From Basics to Advanced Big Data Analysis

Summary

Title: Mastering Hive: From Basics to Advanced Big Data Analysis

Price: $54.99

Average Rating: 4.5

Number of Lectures: 190

Number of Published Lectures: 190

Number of Curriculum Items: 190

Number of Published Curriculum Objects: 190

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will Learn

  • Introduction to Hive: Understand the fundamentals of Hive and its role in the Hadoop ecosystem.
  • Hive Database Management: Learn how to create and manage Hive databases and tables.
  • Data Loading and Manipulation: Master the techniques for loading data into Hive and performing data manipulation operations.
  • Advanced Querying: Execute complex queries using HiveQL, including joins, partitions, and bucketing.
  • Hive Functions: Utilize built-in Hive functions for data processing and analysis.
  • User Defined Functions (UDFs): Create and implement custom UDFs to extend Hive's capabilities.
  • Hive Integration with HBase: Explore the integration of Hive with HBase for efficient data storage and retrieval.
  • Real-World Case Studies: Apply Hive knowledge to practical case studies in various industries, such as telecom and social media.
  • Hive with Other Big Data Tools: Learn to use Hive in conjunction with Pig, MapReduce, and Sqoop for comprehensive data analysis.
  • Sensor Data Analysis: Gain hands-on experience in processing and analyzing sensor data using Hive and Pig.

Who Should Attend

  • Aspiring Data Engineers: Individuals aiming to build a career in data engineering and big data analytics.
  • Big Data Enthusiasts: Anyone with a passion for big data technologies and analytics.
  • Data Analysts: Professionals seeking to enhance their data analysis skills with Hive.
  • Students: Computer science and engineering students interested in learning about big data technologies.
  • IT Professionals: IT professionals looking to upskill and transition into big data roles.
  • Software Developers: Developers wanting to integrate Hive capabilities into their applications.
  • Tech Entrepreneurs: Entrepreneurs looking to implement big data solutions in their startups.

Target Audiences

  • Aspiring Data Engineers: Individuals aiming to build a career in data engineering and big data analytics.
  • Big Data Enthusiasts: Anyone with a passion for big data technologies and analytics.
  • Data Analysts: Professionals seeking to enhance their data analysis skills with Hive.
  • Students: Computer science and engineering students interested in learning about big data technologies.
  • IT Professionals: IT professionals looking to upskill and transition into big data roles.
  • Software Developers: Developers wanting to integrate Hive capabilities into their applications.
  • Tech Entrepreneurs: Entrepreneurs looking to implement big data solutions in their startups.

Students will gain a comprehensive understanding of Hive, from the fundamentals to advanced topics. They will learn how to create and manage Hive databases, perform data loading and manipulation, execute complex queries, and use Hive’s powerful features for data partitioning, bucketing, and indexing. Additionally, students will explore practical case studies and projects, applying their knowledge to real-world scenarios such as telecom industry analysis, customer complaint analysis, social media analysis, and sensor data analysis.

Section 1: Hive – Beginners

In this section, students will be introduced to Hive, an essential tool for managing and querying large datasets stored in Hadoop. They will learn the basics of Hive, including how to create databases, load data, and manipulate tables. Topics such as external tables, the Hive Metastore, and partitions will be covered, along with practical examples of creating partition tables, using dynamic partitions, and performing Hive joins. Students will also explore the concept of Hive UDFs (User Defined Functions) and how to implement them.

Section 2: Hive – Advanced

Building on the foundational knowledge, this section delves into advanced Hive concepts. Students will learn about internal and external tables, inserting data, and various Hive functions. The section covers advanced partitioning techniques, bucketing, table sampling, and indexing. Practical demonstrations include creating views, using Hive variables, and understanding Hive architecture. Students will also explore Hive’s parallelism capabilities, table properties, and how to manage and compress files in Hive.

Section 3: Project 1 – HBase Managed Hive Tables

This section focuses on integrating Hive with HBase, a distributed database. Students will learn how to create and manage Hive tables, both managed and external, and understand the nuances of static and dynamic partitions. They will gain hands-on experience in creating joins, views, and indexes, and explore complex data types in Hive. The section culminates in practical implementation projects involving Hive and HBase, showcasing real-world applications and use cases.

Section 4: Project 2 – Case Study on Telecom Industry using Hive

Students will apply their Hive knowledge to a case study in the telecom industry. This project involves working with simple and complex data types, creating and managing tables, and using partitions and bucketing to organize data. Students will learn how to perform various data operations, understand table control services, and create contract tables. This hands-on project provides valuable insights into how Hive can be used for industry-specific data analysis.

Section 5: Project 3 – Customer Complaints Analysis using Hive – MapReduce

In this section, students will analyze customer complaints data using Hive and MapReduce. They will learn how to create driver files, process data from specific locations, and group complaints by location. This project highlights the power of Hive and MapReduce for handling large datasets and provides practical experience in data processing and analysis.

Section 6: Project 4 – Social Media Analysis using Hive/Pig/MapReduce/Sqoop

This section explores the integration of Hive with other big data tools like Pig, MapReduce, and Sqoop for social media analysis. Students will learn how to process and analyze social media data, perform data transfers from RDMS to HDFS, and execute MapReduce programs. The project includes practical exercises in processing XML files, analyzing book reviews and performance, and working with complex datasets using Hive and Pig.

Section 7: Project 5 – Sensor Data Analysis using Hive/Pig

The final section focuses on sensor data analysis using Hive and Pig. Students will learn the basics of big data and MapReduce, and how to convert JSON files into text format. They will perform various data analysis tasks, including calculating ratios, generating reports, and processing data using Pig functions. This project provides comprehensive hands-on experience in processing and analyzing sensor data, showcasing the practical applications of Hive and Pig in real-world scenarios.

Conclusion

This course provides a complete journey from understanding the basics of Hive to mastering advanced big data analysis techniques. Through a combination of theoretical knowledge and practical projects, students will gain the skills needed to manage, analyze, and derive insights from large datasets using Hive. Whether you’re an aspiring data engineer, a data analyst, or a tech entrepreneur, this course will equip you with the tools and knowledge to excel in the world of big data.

Course Curriculum

Chapter 1: Hive – Beginners

Lecture 1: Introduction to HIVE

Lecture 2: HIVE Data Base

Lecture 3: Load Data Command

Lecture 4: How to Replace Column

Lecture 5: External Table

Lecture 6: HIVE Metastore

Lecture 7: what is Hive Partition

Lecture 8: Creating Partition Table

Lecture 9: Insert Overwrite Table

Lecture 10: Dynamic Partition True

Lecture 11: Hive Bucketing

Lecture 12: Decomposing Data Sets

Lecture 13: Hive Joins

Lecture 14: Hive Joins Continue

Lecture 15: Skew Join

Lecture 16: What is Serde

Lecture 17: Serde in Hive

Lecture 18: Hive UDF

Lecture 19: Hive UDF Continues

Lecture 20: More Hive UDF

Lecture 21: Maxcale Function

Lecture 22: Hive Example Use Case

Chapter 2: Hive – Advanced

Lecture 1: Introduction to Hive Concepts and Hands-on Demonstration

Lecture 2: Internal Table and External Table

Lecture 3: Inserting Data Into Tables

Lecture 4: Date and Mathematical Functions

Lecture 5: Conditional Statements

Lecture 6: Explode and Lateral View

Lecture 7: Sorting

Lecture 8: Join

Lecture 9: Map Join

Lecture 10: Static and Dynamic Partitioning

Lecture 11: More on Dynamic Partitioning

Lecture 12: Alter Command

Lecture 13: MSCK Command

Lecture 14: Bucketing

Lecture 15: Table Sampling

Lecture 16: Archiving

Lecture 17: Ranks

Lecture 18: Creating Views

Lecture 19: Advantages of views and Altering Views

Lecture 20: What is Indexing

Lecture 21: Compact and Bitmap Index Running Time

Lecture 22: Hive Commands in Bash Shell

Lecture 23: Hive Variables – Hiveconf

Lecture 24: Hive Variables -Hiveconf in Bash Shell

Lecture 25: Configuring a Hive Var Variable

Lecture 26: Variable Substitution

Lecture 27: Word Count

Lecture 28: Hive Architecture

Lecture 29: Parallelism in Hive

Lecture 30: Table Properties in Hive

Lecture 31: Null Format Properties

Lecture 32: Null Format Properties Continues

Lecture 33: Purge Commands in Hives

Lecture 34: Slowing Changing Dimension

Lecture 35: Implement the SCD

Lecture 36: Example of the SCD

Lecture 37: How to Load XML Data in Hive

Lecture 38: How to Load XML Data in Hive Continue

Lecture 39: No Drop and Offline in Hive

Lecture 40: Immutable Table

Lecture 41: How to Create Hive RC File

Lecture 42: Multiple Tables

Lecture 43: Merging Hive Created Files and Function rLike

Lecture 44: Various Configuration Settings in Hive

Lecture 45: Various Configuration Settings in Hive Continues

Lecture 46: Compressing Various Files in Hive

Lecture 47: Different Modes in Hive

Lecture 48: File Compression in Hive

Lecture 49: Type of Mode in Hive

Lecture 50: Comparison of Internal and External Table

Chapter 3: Project1 – HBase Managed HIVE Tables

Lecture 1: Introduction to Hive

Lecture 2: Creating Hive Tables

Lecture 3: Managed Tables in Hive

Lecture 4: External Tables in Hive

Lecture 5: More on External Tables in Hive

Lecture 6: Tables with Location

Lecture 7: Static Partitions

Lecture 8: Dynamic Partitions

Lecture 9: Dynamic Partitions Continues

Lecture 10: Adding Partitions

Lecture 11: File Formats

Lecture 12: Bucketing and its Code in Hive

Lecture 13: Introduction to Joins in Hive

Lecture 14: Example of Joins in Hive

Lecture 15: Creating a Join Space in Hive

Lecture 16: Creating a Join Space in Hive Continue

Lecture 17: Views and it Example in Hive

Lecture 18: Indexes

Lecture 19: Examples of Index

Lecture 20: Complex Data Types

Lecture 21: Complex Data Types Continues

Lecture 22: Examples of Data Types in Hive

Lecture 23: Three Types Data

Lecture 24: Hive Scripts and its Example

Lecture 25: User Defined Function And its Advantages in Hive

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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