Unique features of HQL: PARTITIONED BY, STORED AS, DISTRIBUTED BY/GROUPED BY, SIDE VIEW with EXPLODE and COLLECT_SET
In most technology companies, data teams must have strong capabilities to manage and process large volumes of data. Therefore, it is essential that these teams are familiar with the Hadoop ecosystem. 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 accessible and easy to use for a wide range of users. If you are already proficient in SQL, you will probably find it easy to transition to HQL. However, it is important to note that HQL includes quite a few unique features and functions that are not available in standard SQL. In this article, I will explore some of these key features and functions of HQL that require specific knowledge beyond SQL based on my prior 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 will provide use cases with simulated data…
