Hive courses can help you learn data warehousing, data querying with HiveQL, and integrating Hive with Hadoop. You can build skills in optimizing query performance, managing large datasets, and transforming data for analysis. Many courses introduce tools like Apache Hadoop and Apache Spark, showing how they work alongside Hive to process and analyze big data efficiently.

Skills you'll gain: Data Storytelling, Data Wrangling, Data Presentation, Big Data, Interactive Data Visualization, Data Analysis, Statistical Visualization, Data Cleansing, Apache Hadoop, Statistical Analysis, Data Visualization, Data Import/Export, Apache Hive, Data Mart, Data Processing, Data Warehousing, Data Transformation, Apache Spark, Data Science, Microsoft Excel
★ 4.8 (21K) · Beginner · Course · 1 - 3 Months

Johns Hopkins University
Skills you'll gain: Apache Hadoop, Big Data, Apache Hive, Apache Spark, NoSQL, Data Infrastructure, File Systems, Data Processing, Data Management, Analytics, Data Science, Databases, Data Integration, SQL, Query Languages, File I/O, Data Architecture, Data Manipulation, Distributed Computing, Performance Tuning
★ 4.6 (9) · Intermediate · Specialization · 3 - 6 Months

Skills you'll gain: Apache Hive, Apache Hadoop, Data Warehousing, SQL, Database Development, Performance Tuning, Query Languages, Database Management, Extensible Markup Language (XML), Data Processing, Data Transformation, Data Management, Data Storage
Mixed · Course · 1 - 3 Months

Coursera
Skills you'll gain: Retrieval-Augmented Generation, Generative AI, LangChain, LLM Application, Large Language Modeling, Embeddings, Vector Databases, AI Orchestration, Model Evaluation
Intermediate · Course · 1 - 4 Weeks

Cloudera
Skills you'll gain: Database Design, SQL, Apache Hive, Relational Databases, Databases, Database Management, Database Management Systems, Data Store, Big Data, Database Systems, Amazon Web Services, MySQL, Data Management, Query Languages, Amazon S3, Data Storage, Data Access, NoSQL, Cloud Storage, Data Analysis
★ 4.7 (1.4K) · Beginner · Specialization · 3 - 6 Months

University of California San Diego
Skills you'll gain: Apache Spark, Model Evaluation, Apache Hadoop, Data Integration, Exploratory Data Analysis, Big Data, Classification Algorithms, Graph Theory, Data Pipelines, Data Processing, Network Model, Model Training, Database Design, Data Modeling, Regression Analysis, Data Management, Data Infrastructure, Data Presentation, Data Mining, MongoDB
★ 4.5 (14K) · Beginner · Specialization · 3 - 6 Months

Cloudera
Skills you'll gain: SQL, Apache Hive, Big Data, MySQL, Query Languages, Databases, Analytics, PostgreSQL, Data Manipulation, Data Integration, Data Analysis, Data Management, Virtual Machines
★ 4.8 (531) · Beginner · Course · 1 - 3 Months

Skills you'll gain: NoSQL, Apache Spark, Apache Hadoop, MongoDB, Database Development, Database Systems, Databases, Database Management Systems, Database Management, Extract, Transform, Load, Database Software, Database Administration, PySpark, Apache Hive, Machine Learning Methods, Big Data, Machine Learning, Applied Machine Learning, Generative AI, Model Evaluation
★ 4.5 (840) · Beginner · Specialization · 3 - 6 Months

Skills you'll gain: Apache Hadoop, Apache Hive, Extract, Transform, Load, Data Import/Export, HR Tech, Data Pipelines, People Analytics, Big Data, Data Migration, Data Integration, MySQL, Data Storage, Data Processing, Data Access, Authentications, SQL, Analytics, Database Management, Relational Databases, Data Architecture
Beginner · Specialization · 1 - 3 Months

Pearson
Skills you'll gain: PySpark, Apache Hadoop, Apache Spark, Big Data, Apache Hive, Data Lakes, Analytics, Data Pipelines, Data Processing, Data Import/Export, Linux Commands, Linux, File Systems, Data Management, Distributed Computing, Command-Line Interface, Relational Databases, Software Installation, Java, C++ (Programming Language)
Intermediate · Specialization · 1 - 4 Weeks

Skills you'll gain: Apache Kafka, Apache Hadoop, Apache Spark, Real Time Data, Scala Programming, Data Integration, Command-Line Interface, Apache Hive, Big Data, Applied Machine Learning, Data Processing, System Design and Implementation, Apache Cassandra, Data Pipelines, Java, Distributed Computing, IntelliJ IDEA, Java Programming, Application Deployment, Enterprise Application Management
★ 4.6 (15) · Intermediate · Specialization · 3 - 6 Months

Università di Napoli Federico II
Skills you'll gain: NoSQL, Control Systems, Apache Hadoop, Apache Hive, Big Data, Machine Controls, Simulation and Simulation Software, Model Based Systems Engineering, Database Systems, Artificial Intelligence, Data Architecture, Data Infrastructure, Mechanical Engineering, Artificial Intelligence and Machine Learning (AI/ML), Computer Vision, Systems Architecture, Simulations, Global Positioning Systems, Business Intelligence, Robotics
★ 4.2 (87) · Beginner · Specialization · 1 - 3 Months
Apache Hive is a software program for data warehouse applications that seek to harness petabyte-scale datasets. It allows for the fast reading, writing, and managing of data on a big data scale, including the ability to project structure onto unstructured datasets that are already in storage. Hive has thus become an important tool to enable data scientists and data engineers to conduct typical data warehousing activities like extract/transform/load (ETL), reporting, and data analysis with today’s extraordinarily large and complex datasets.
While Hive allows for the use of an SQL interface for queries similar to those used by traditional, centralized database management systems (DMBS), it is built to work with distributed file systems that integrate with the popular, open-source Apache Hadoop framework. Thus, it offers maximum scalability, performance, fault tolerance, and loose-coupling with input formats, and works with other programs in the Apache ecosystem like Apache Spark and MapReduce.‎
Today’s data engineers must work with increasingly unwieldy datasets, creating data infrastructure that can take structured, unstructured, and real-time big data-scale datasets and deliver them to data scientists and the software applications they build in a usable form. Thus, Hive has become indispensable for many data engineers, especially those working at leading tech companies that may pull data from diverse sources such as shopping histories, social media activity, geographic location, and more.
Unsurprisingly, data engineers capable of working on these big data projects are in high demand - and are highly paid. According to Glassdoor, the average base salary for data engineers is $102,864 per year.‎
Absolutely! Coursera is a fantastic place to build a wide variety of computer science and data science skills, including working with Hive and the rest of the Apache Hadoop ecosystem. You can learn from top-ranked schools like University of California San Diego, industry leaders like Cloudera and Yandex, or by completing step-by-step tutorials alongside experienced instructors as part of Coursera’s Guided Projects. Regardless of how you choose to learn, Coursera lets you view course materials and complete assignments on a flexible schedule, ensuring you can pick up valuable skills in Hive and other data science topics alongside your existing studies or career.‎
The skills or experience you need to already have, before starting to learn Hive may include a good knowledge of the programming language SQL as well as Apache Hadoop, which is an open-source framework that is used in Hive to store and process large datasets. Having a good grasp of data warehousing software, in general, would also help you to learn what it takes to work with Hive. These would be beneficial to you for learning how to read, write, or manage large sets of data files that are the basis of Apache Hive.‎
The background of the people best suited for work that involves Hive would include computer literacy, strong data warehousing skills, an understanding of big data and machine learning, and knowledge of optimizing and debugging applications. Hive workers are skilled to work with large graphs, SQL queries, data analyzation, and optimization. These focused data scientists are often at the leading edge of data storage applications, and working with big data analyses. This is why they can command six-figure and higher salaries in the right circumstances.‎
Topics related to Hive that you can study would include data warehousing, data infrastructure, SQL programming, database management systems, and understanding applications written in C++, Java, PHP, Python, or Ruby. You may also want to dig into understanding Spark, which is a fast and general engine for large-scale data processing. Spark is used in conjunction with Hive as a distributed processing system used for big data workloads.‎
To know if learning Hive is right for you, you should be excited about working with big data, with a strong focus on numbers, data, and how to delineate data patterns. You might also have a passion for writing code. When learning Hive, this would help you to write Hive Query Language (HiveQL) statements that would be used for data query and analysis. If you’re interested in data sciences and data warehousing in large organizations, it would be beneficial for you to learn Hive.‎