Must-Know Techniques for Handling Big Data in Hive

In most tech companies, data teams must possess strong capabilities to manage and process big data. As a result, familiarity with the Hadoop ecosystem is essential for these teams. Hive Query Language (HQL), developed by Apache, is a powerful tool for data professionals to manipulate, query, transform, and analyze data within this ecosystem.

HQL offers a SQL-like interface, making data processing in Hadoop both accessible and user-friendly for a broad range of users. If you’re already proficient in SQL, you’ll likely find it not challenging to transition to HQL. However, it’s important to note that HQL includes quite a few unique functions and features that aren’t available in standard SQL. In this article, I’ll explore some of these key HQL functions and features that require specific knowledge beyond SQL based on my previous experience. Understanding and utilizing these capabilities is critical for anyone working with Hive and big data, as they form the backbone of building scalable and efficient data processing pipelines and analytics systems in the Hadoop ecosystem. To illustrate these concepts, I’ll provide use cases with mock data…