HQL’s Distinctive Options— PARTITIONED BY, STORED AS, DISTRIBUTE BY / CLUSTER BY, LATERAL VIEW with EXPLODE and COLLECT_SET
In most tech corporations, information groups should possess robust capabilities to handle and course of large information. Because of this, familiarity with the Hadoop ecosystem is important for these groups. Hive Question Language (HQL), developed by Apache, is a robust instrument for information professionals to control, question, remodel, and analyze information inside this ecosystem.
HQL provides a SQL-like interface, making information processing in Hadoop each accessible and user-friendly for a broad vary of customers. In the event you’re already proficient in SQL, you’ll seemingly discover it not difficult to transition to HQL. Nevertheless, it’s vital to notice that HQL consists of fairly a number of distinctive capabilities and options that aren’t out there in customary SQL. On this article, I’ll discover a few of these key HQL capabilities and options that require particular data past SQL based mostly on my earlier expertise. Understanding and using these capabilities is crucial for anybody working with Hive and large information, as they type the spine of constructing scalable and environment friendly information processing pipelines and analytics techniques within the Hadoop ecosystem. For instance these ideas, I’ll present use instances with mock information…