Apache Hive is a data warehouse infrastructure built on top of Hadoop. It facilitates querying and managing large datasets residing in distributed storage systems. With the increasing volume and complexity of data, there is a growing demand for professionals proficient in Apache Hive. To cater to this demand, several online courses are available that offer training in Apache Hive. In this article, we will explore some of the best Apache Hive courses available online.
Here’s a look at the Best Apache Hive Courses and Certifications Online and what they have to offer for you!
10 Best Apache Hive Courses and Certifications Online
- 10 Best Apache Hive Courses and Certifications Online
- 1. Learning Apache Hadoop EcoSystem- Hive by Balaji M (Udemy) (Our Best Pick)
- 2. Hive to ADVANCE Hive (Real time usage) :Hadoop querying tool by J Garg (Udemy)
- 3. From 0 to 1: Hive for Processing Big Data by Loony Corn (Udemy)
- 4. Big Data Analyst -using Sqoop and Advance Hive (CCA159) by Navdeep Kaur (Udemy)
- 5. Big Data Internship Program – Data Processing – Hive and Pig by Big Data Trunk (Udemy)
- 6. Data Analysis Made Easy – Learn Hive & Pig by Abhishek Roy (Udemy)
- 7. Comprehensive Course on Hadoop Analytic Tool : Apache Hive by Easylearning guru (Udemy)
- 8. Advance Hadoop and Hive for Testers by Lead Big Data Engineer (Udemy)
- 9. Hive in Depth Training and Interview Preparation course by E Learn Analytics (Udemy)
- 10. An Advanced Guide for Apache Hive: A Hadoop Ecosystem Tool by Launch Programmers (Udemy)
1. Learning Apache Hadoop EcoSystem- Hive by Balaji M (Udemy) (Our Best Pick)
Course Title: Learning Apache Hadoop EcoSystem- Hive
Course Instructors: Balaji M
Course Short Description: Learn Apache Hive and Start working with SQL queries which is on Data which is in Hadoop
Course Long Description: This course offers comprehensive training on Apache Hive, an SQL-based data warehousing platform that runs on Hadoop. Designed to meet the learning requirements of data professionals, the course is updated regularly with new tutorials based on the needs of participants.
The course begins with an overview of the need for Hive architecture and various configuration parameters in Hive. Participants will learn about the different aspects of Hive and how it integrates as a data warehousing platform on Hadoop. The instructor recommends subscribing to his Youtube Channel “Hadooparch” for more details.
The course covers the SQL of Hadoop (HQL) and discusses the reasons for installing and configuring Hive on Hadoop. Participants will also learn about the components and architecture of Hive and how it stores data in table-like structures over HDFS data. The course covers the installation and configuration of Hive server2, replacing the PostgreSQL database with MySQL, and how to install MySQL and configure it as Hive Metastore.
The course is packed with Hive demonstrations, including how to create databases, understand data types, create external, internal, and partitioned Hive tables, bucketing, load data from the local and distributed filesystem (HDFS), setup dynamic partitioning, create views, and manage indexes. The course also covers the different roles involved in implementing real-time projects, project setup and permissions, auditing, and troubleshooting.
Finally, the instructor provides sample data and queries for participants to work on and replicate what has been taught in the videos. The course comes with multiple questions to test participants’ understanding, which they are encouraged to attempt.
This course, titled “Hive to ADVANCE Hive (Real time usage),” offers instruction on Apache Hive, a data processing tool for Hadoop. The course is designed to provide knowledge on Basic Hive to Advance Hive (Real-Time concepts), including use cases asked in interviews. The course covers topics such as variables in Hive, table properties of Hive, custom input formatter, map and bucketed joins, advance functions in Hive, compression techniques in Hive, configuration settings of Hive, working with multiple tables in Hive, and loading unstructured data in Hive, among others. The course also includes a section on frequently asked use cases with their practical workings in Hive. The course is made with real implementation of Hive in live projects in mind. Additionally, a step-by-step installation guide (pdf) is available for download to install Hadoop and Apache Hive. The course content is divided into sections, including Introduction (Theory), Hive Basic Commands, Functions in Hive, Partitioning in Hive, Bucketing in Hive, Joins in Hive, Views in Hive, Indexing (Advance), UDF’s (User defined functions) Advance, Table Properties (Advance), Configurations & Settings in Hive (Advance), Variables in Hive (Advance), Different Types of Files in Hadoop, Custom Input Formatter (Advance), Miscellaneous (Advance), TEZ engine in Hive, Load XML data in Hive, and Implementing SCD’s in Hive (Advance).
The “From 0 to 1: Hive for Processing Big Data” course is taught by a team of four instructors, including two ex-Googlers and two ex-Flipkart Lead Analysts with decades of practical experience in working with large-scale data.
This end-to-end course provides a practical guide to using Hive for Big Data processing. Hive is similar to SQL, and the course will take you through all the gaps between SQL and what you need to use Hive.
The course is suitable for both analysts who want to process data and engineers who need to build custom functionality or optimize performance. If you’re new to SQL, the course includes a primer on all the basic SQL constructs.
The course covers analytical processing, tuning Hive for better functionality, and custom functions in Python and Java. Topics include Joins, Subqueries, Views, Table Generating Functions, Explode, Lateral View, Windowing, Partitioning, Bucketing, Join Optimizations, Map Side Joins, Indexes, and more.
The course is taught using real-life examples, working queries, and code. The SQL primer includes Select Statements, Group By, Order By, and Having.
The course also includes sections introducing Hive, installing Hadoop and Hive, providing an overview of Hadoop and HDFS, and understanding MapReduce.
Overall, the “From 0 to 1: Hive for Processing Big Data” course is a comprehensive and practical guide for using Hive in Big Data processing.
The course titled “Big Data Analyst – using Sqoop and Advance Hive (CCA159)” is designed to teach participants how to become a Big Data Analyst using Hive and Sqoop. This course is ideal for business analysts, testers, and SQL developers.
The course begins by introducing participants to Hadoop and the Hadoop distributed file system, along with the most common Hadoop commands required to work with the Hadoop file system. Participants will then be introduced to Sqoop Import and will learn about the lifecycle of Sqoop command.
During the course, participants will learn how to use the Sqoop import command to migrate data from MySQL to HDFS and Hive. They will also learn about various file formats, compressions, file delimiter, where clause, and queries while importing the data. Additionally, participants will understand split-by and boundary queries and use the incremental mode to migrate the data from MySQL to HDFS.
Participants will also learn about Sqoop Export and how to use it to migrate data from HDFS and Hive to MySQL. The course will conclude with Apache Hive [Advance], which will cover topics such as External & Managed Tables, Insert & Multi Insert, Data Types & Complex Data Types, Collection Function, and Conditional Function.
The course will also cover Hive String Functions, Hive Date Functions, Mathematical Functions, Hive Analysis, Alter Command, Joins, Multi Joins & Map Joins, Working with Different Files – Parquet, Avro, Compressions, Partitioning, Bucketing, Views, Lateral Views/Explode, and Windowing Functions – Rank/Dense Rank/lead/lag/min/max. The content of the course is divided into sections, including Hadoop Introduction, Hive, Hive Data Types, Hive Functions, Hive Join, Working with Different File Formats & Compressions, Advance Hive, Hive Windows Function, Sqoop Import, and Sqoop Export.
The Big Data Internship Program offers a course on Data Processing with Hive and Pig, which provides higher-level language to facilitate large-data processing. The course is part of the program’s alignment with a typical Big data project life cycle stage, specifically the processing stage.
The course is designed for developers, data analysts, and business analysts with prior experience in SQL and scripting languages. The course offers lessons on Hive core concepts and architecture, table creation and manipulation, advanced features, best practices, and real-time, complex queries on datasets. Additionally, the course covers Pig’s architecture, reading and writing data, and best practices.
The course offers a project work component where students provide data in Hive and manipulate it for the “Our Book Recommendation” project. Additionally, students undertake an add-on project on data masking with Hive and Sqoop.
The course is divided into sections, including an introduction to data processing in Big Data, Hive, Pig, data processing in the recommendation project, and an add-on project on data masking.
The “Data Analysis Made Easy – Learn Hive & Pig” course is designed to teach data analysis using Pig and Hive through hands-on examples and well-explained concepts. The course covers Hadoop, Pig, Hive, and Apache Mahout from scratch with an example-based and hands-on approach. The course aims to help learners understand the fundamental concepts of data analysis using Hive & Pig and the landscape of Big Data and Apache Hadoop.
The course includes 19 lectures and 3 hours of content, and no prior knowledge of Hadoop, Hive, or Pig is expected. A bit of procedural programming and querying experience will help learners derive real value from the course. The course focuses on easy-to-use Hive and Pig technologies to help learners land prestigious and well-paying Big Data Analyst jobs.
The first few topics of the course focus on the rise of Big Data and how Apache Hadoop fits in. The course then moves on to the fundamentals of Hadoop and its core components: HDFS and MapReduce. Once the learners have a solid foundation of Hadoop and the ecosystem, the course dives into the higher-level components of the Hadoop ecosystem: Hive and Pig. The course covers the details of both Hive and Pig by installing them and working with examples.
Hive and Pig can make the life of a data analyst easy by shielding them from the complexity of writing MR jobs and yet leveraging the parallel processing ability of the Hadoop framework. After taking the course, learners will be at ease with analyzing data with Hive and Pig. The course includes slides, examples, code, and data sets to help learners master the concepts of data analysis using Hive & Pig.
The “Data Analysis Made Easy – Learn Hive & Pig” course includes the following sections: Course Introduction, Introduction to Big Data, Introduction to Hadoop, HDFS & MapReduce overview, Hadoop Installation, Hive, and Pig.
The Comprehensive Course on Hadoop Analytic Tool, Apache Hive, is designed to provide learners with knowledge about analyzing large data sets and hiding the complexity of MapReduce Programs. The course consists of 53 lectures, totaling 9 hours of video content, and includes quizzes and additional exercises for learners to practice and test their knowledge.
Upon completing the course, learners will be proficient in accessing, handling, manipulating, and analyzing large data sets present in the Hadoop cluster. They will also be able to solve real-life case studies and work on projects with live data. Writing HiveQL statements and UDFs will become easier after the course completion, as the course provides high-quality content to acquire essential skills needed to pertain the knowledge of this analytic tool.
This course is intended for developers who want to analyze large and complex data sets, software professionals, analytic professionals, and ETL developers. It is suitable for anyone who wants to begin working with humongous and complex data with comfort and ease.
The course content is divided into sections, including Introduction, Hive Data Types and DDL, Hive DML and HiveQL, Hive Queries and Views, Hive Indexing and Tuning, Compression with Hive, UDFs in Hive, Customizing Hive File and Record Formats, Hive Storage Handlers and NoSQL, HCatalog, and Quizzes.
Overall, the Comprehensive Course on Hadoop Analytic Tool, Apache Hive, is a comprehensive and in-depth training program suitable for anyone interested in analyzing large and complex data sets using the Apache Hive tool.
The Advance Hadoop and Hive for Testers course is designed for students in the testing profile who wish to pursue a career in big data testing. The course covers Hadoop and Hive and is intended for users in the QA profile who want to transition into big data testing. The tutorials provide advanced knowledge and are recommended for students who wish to learn from scratch.
The course material includes the necessary content for big data testing and covers Hadoop, Hive, and Unix. It provides detailed information on different Hadoop and Hive commands required by testers to progress into the Big Data Testing domain. The course is well-structured with practical sessions separated by different topics.
Students will learn about Hadoop Introduction, Cloudera Setup Process, HDFS Practical Sessions, Hive Practical Sessions, and Unix Practical Sessions. The course is designed to provide students with a comprehensive understanding of Hadoop, Hive, and advanced Unix commands.
The Hive in Depth Training and Interview Preparation course offered by E Learn Analytics provides thorough training on Apache Hive concepts along with commonly asked interview questions and their answers. The course aims to equip learners with in-depth knowledge of Hive and how to use it in real-world scenarios. Multiple examples are included to demonstrate the concepts and their practical use cases.
The course contains more than 200 interview questions at various difficulty levels, covering topics such as Hive Architecture and Basics, Hive DDL, Hive DML, File Formats and Data Types, Schema Design, Query Tuning, Hive Functions, Thrift Services, NoSQL, Storage Handlers, Hive Security, and Locking, and HCatalog.
The course is divided into several sections, including Introduction, Hive Overview and Architecture, Getting Started, File Formats and Data Types, Data Definition, Data Manipulation, Hive QL & Queries, Views in Hive, Schema Design, Query Tuning, Other File Formats and Compression, Hive Functions and UDF, Hive Thrift Services, Storage Handlers and NOSQL, Security and Locking, HCatalog, and Misc Interview Questions.
Overall, the Hive in Depth Training and Interview Preparation course is designed to provide learners with a comprehensive understanding of Hive and how to use it effectively in various use cases, while also preparing them for interviews with a vast collection of interview questions and their answers.
The course titled “An Advanced Guide for Apache Hive: A Hadoop Ecosystem Tool” is aimed at teaching participants about the SQL Layer on Hadoop, which is a data warehouse infrastructure tool used to process structured data in Hadoop. The course instructors are Launch Programmers.
The course covers several topics related to Apache Hive, including building tables and databases to analyze Big Data, installing and managing the Hadoop cluster on the cloud, writing UDFs to solve complex problems, querying and managing large datasets in distributed storage, transforming unstructured and semi-structured data into usable schema-based data, and writing HiveQL statements as one would write a MapReduce program in any host language.
The course is designed to provide practical, hands-on experience with real case studies and live data from Twitter. It is divided into ten modules, including an introduction to the course, Hive architecture, Hive Query Language, and modules four through ten. The course aims to equip participants with the skills and knowledge required to use Apache Hive effectively in processing big data.