Apache Hive Explained in 5 Minutes or Less [+5 Learning Resources]

Apache Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale.

A data warehouse is a data management system that stores large amounts of historical data derived from various sources for the purpose of data analysis and reporting. This, in turn, supports business intelligence leading to more informed decision-making.

The data used in Apache Hive is stored in Apache Hadoop, an open-source data storage framework for distributed data storage and processing. Apache Hive is built on top of Apache Hadoop and thus stores and extracts data from Apache Hadoop. However, other data storage systems, such as Apache HBase, can also be used.

The best thing about Apache Hive is that it allows users to read, write and manage large datasets and query and analyze the data using Hive Query Language(HQL), similar to SQL.

How Apache Hive Works

Apache Hive provides a high-level, SQL-like interface for querying and managing large amounts of data stored in the Hadoop Distributed File System(HDFS). When a user executes a query in Apache Hive, the query is translated into a series of MapReduce jobs executed by the Hadoop cluster.

MapReduce is a model for processing large amounts of data in parallel acrosses distributed clusters of computers. Once the MapReduce jobs are completed, their results are processed and combined to produce a single final result. The final result can be stored in a Hive table or exported to HDFS for further processing or analysis.

Queries in Hive can be executed faster by using partitions to divide Hive tables into different parts based on the table information. These partitions can be broken down even further to allow very fast querying of large data sets. This process is known as bucketing.

Apache Hive is a must-have for organizations working with big data. This is because it allows them to easily manage large datasets, process the data in a very fast manner and easily perform complex data analysis on the data. This leads to comprehensive and detailed reports from available data allowing for better decision-making.

Benefits of Using Apache Hive

Some of the benefits of using Apache Hive include the following:

Easy to use

By allowing querying of data using HQL, similar to SQL, using Apache Hive becomes accessible to programmers and non-programmers alike. Therefore, data analysis can be done on large data sets without learning any new language or syntax. This has been a key contributor to the adoption and use of Apache Hive by organizations. 

Fast

Apache Hive allows for very fast data analysis of large datasets through batch processing. In batch processing, large datasets are collected and processed in groups. The results are later combined to produce the final results. Through batch processing, Apache Hive allows for fast processing and data analysis.

Reliable

Hive uses the Hadoop Distributed File System(HDFS) for data storage. By working together, data can be replicated when it is being analyzed. This creates a fault-tolerant environment where data cannot be lost even when computer systems malfunction.

This allows Apache Hive to be very reliable and fault-tolerant, which makes it stand out among other data warehouse systems.

Scalable

Apache Hive is designed in a manner that allows it to scale and handle increasing datasets easily. This provides users with a data warehouse solution that scales according to their needs.

Cost-effective

Compared to other data warehousing solutions, Apache Hive, which is open source, is relatively cheaper to run and, thus, the best option for organizations keen on minimizing the costs of operations being profitable.

Apache Hive is a robust and reliable data warehousing solution that not only scales according to a user’s needs but also provides a fast, cost-effective, and easy-to-use data warehousing solution.

Apache Hive Features

Key features in Apache hive include:

#1. Hive Server 2(HS2)

It supports authentication and multi-client concurrency and is designed to offer better support for open API clients like Java Database Connectivity(JDBC) and Open Database Connectivity (ODBC).

#2. Hive Metastore Server(HMS)

HMS acts as a central store for the metadata of Hive Tables and partitions for a relational database. The metadata stored in HMS is made available to clients using metastore service API.

#3. Hive ACID

Hive ensures that all transactions done are ACID compliant. ACID represents the four desirable traits of database transactions. This includes atomicity, consistency, isolation, and durability.

#4. Hive Data Compaction

data compaction is the process of reducing the data size that is stored and transmitted without compromising the quality and integrity of the data. This is done by removing redundancy and irrelevant data or using special encoding without compromising the quality and integrity of the data being compacted. Hive offers out-of-the-box support for data compaction.

#5. Hive Replication

Hive has a framework that supports the replication of Hive metadata and data changes between clusters for the purpose of creating backups and data recovery.

#6. Security and Observability

Hive can be integrated with Apache Ranger, a framework that enables monitoring and managing data security, and with Apache Atlas, which enables enterprises to meet their compliance requirements. Hive also supports Kerberos authentication, a network protocol that secures communication in a network. The three together make Hive secure and observable.

#7. Hive LLAP

Hive has Low Latency Analytical Processing (LLAP) which makes Hive very fast by optimizing data caching and using persistent query infrastructure.

#8. Cost-based Optimization

Hive uses a cost-based query optimizer and query execution framer by Apache Calcite to optimize its SQL queries. Apache Calcite is used in building databases and data management systems.

The above features make Apache Hive an excellent data warehouse system

Use Cases For Apache Hive

Apache Hive is a versatile data warehouse and data analysis solution that allows users to easily process and analyze large amounts of data. Some of the use cases for Apache Hive include:

Data Analysis

Apache Hive supports the analysis of large data sets using SQL-like statements. This allows organizations to identify patterns in the data and draw meaningful conclusions from extracted data. This is useful in design making. Examples of companies that use Apache Hive for data analysis and querying include AirBnB, FINRA, and Vanguard.

Batch Processing

This involves using Apache Hive to process very large datasets through distributed data processing in groups. This has the advantage of allowing fast processing of large datasets. An example of a company that uses Apache Hive for this purpose is Guardian, an insurance and wealth management company.

Data Warehousing

this involves using Apache hive to store and manage very large datasets. In addition to this, the data stored can be analyzed, and reports generated from the. Companies that use Apache Hive as a data warehouse solution include JPMorgan Chase and Target.

Marketing and customer analysis

organizations can use Apache Hive to analyze their customer data, perform customer segmentation and be able to understand their customers better, and tweak their marketing efforts to match their understanding of their customers. This is an application that all companies that handle customer data can use Apache Hive for.

ETL(Extract, Transform, Load) processing

When working with a lot of data in a data warehouse, it is necessary to perform operations such as data cleaning, extraction, and transformation before data can be loaded and stored in a data warehouse system.

This way, data processing and analysis will be fast, easy, and error-free. Apache Hive can perform all these operations before data is loaded into a data warehouse.

The above make up the main uses cases for Apache Hive

Learning Resources

Apache hive is a very useful tool for data warehousing and data analysis of large datasets. Organizations and individuals working with large datasets stand to benefit by using Apache hive. To learn more about Apache Hive and how to use it, consider the following resources:

#1. Hive To ADVANCE Hive (Real-time usage)

Hive to Advance Hive is a best-selling course on Udemy created by J Garg, a senior big data consultant with over a decade of experience working with Apache technologies for data analysis and training other users.

This is a one-of-a-kind course that takes learners from the basics of Apache Hive to advanced concepts and also includes a section on use cases used in Apache Hive Job interviews. It also provides data sets and Apache Hive queries that learners can use to practice while learning.

Some of the Apache Hive concepts covered include advanced functions in Hive, compression techniques in Hive, configuration settings of Hive, working with multiple tables in Hive, and loading unstructured data in Hive. 

The strength of this course lies in the in-depth coverage of advanced Hive concepts used in real-world projects.

#2. Apache Hive For Data Engineers

This is a hands-on, project-based Udemy Course that teaches learners how to work with Apache Hive from a beginner level to an advanced level by working on real-world projects.

The course starts with an overview of Apache Hive and covers why it is a necessary tool for data engineers. It then explores the Hive architecture, its installation, and the necessary Apache Hive configurations. After laying the foundation, the course proceeds to cover hive query flows, hive features, limitations, and the data model used in Apache hive.

It also covers data type, data definition language, and data manipulation language in Hive. The final sections cover advanced Hive concepts such as views, partitioning, bucketing, joins,  and built-in functions and operators.

To cap it all,  the course covers frequently asked interview questions and answers. This is an excellent course to learn about Apache Hive and how it can be applied in the real world.

#3. Apache Hive Basic to advance

Apache Hive Basic to advance is a course by Anshul Jain, a senior data engineer with tons of experience working with Apache Hive and other Big data tools. 

This presents Apache Hive concepts in an easy-to-understand manner and is suitable for beginners looking to learn the ropes of Apache Hive.

The course covers HQL clauses, window functions, materialized view, CRUD operations in Hive, exchange of partitions, and performance optimization to allow fast data querying.

This course will give you a hands-on experience with Apache Hive in addition to helping tackle common interview questions you’re likely to encounter when applying for a job.

#4. Apache Hive Essentials

This book is particularly useful to data analysts, developers, or anyone interested in learning how to use Apache Hive.

The author has over a decade of experience working as a big data practitioner designing and implementing enterprise big data architecture and analytics in various industries.

The book covers how to create and set up a Hive environment, effectively describe data using Hive’s definition language, and join and filter data sets in Hive.

Additionally, it covers data transformations using Hive sorting, ordering, and functions, how to aggregate and sample data, and how to boost the performance of Hive queries and enhance security in Hive. Finally, it covers customizations in Apache hive, teaching users how to tweak Apache Hive to serve their big data needs.

#5. Apache Hive Cookbook

Apache Hive Cookbook, available in Kindle and paperback, provides an easy-to-follow, hands-on take on Apache Hive, allowing you to learn and understand Apache Hive and its integration with popular frameworks in big data.

This book, intended for readers with prior knowledge of SQL, covers how to configure Apache Hive with Hadoop, services in Hive, the Hive data model, and Hive data definition and manipulation language.

Additionally, it covers extensibility features in Hive, joins and join optimization, statistics in Hive, Hive functions, Hive tuning for optimization, and security in Hive, and concludes with in-depth coverage of the integration of Hive with other frameworks.

Conclusion

It is worth noting that Apache Hive is best used for traditional data warehousing tasks and unsuitable for processing online transactions. Apache is designed to maximize performance, scalability, fault tolerance, and loose coupling with its input formats.

Organizations that handle and process large amounts of data stand to benefit tremendously from the robust features offered by Apache Hive. These features are very useful in storing and analyzing large datasets.

You may also explore some major differences between Apache Hive and Apache Impala.