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.