Data Wrangling with Hive: Managing Big Data Efficiently

This lecture focuses on data wrangling techniques using Apache Hive within the context of big data management. The instructor begins by reviewing the previous week's concepts related to Hadoop Distributed File Systems (HDFS) and the challenges of handling large datasets. The session emphasizes the importance of querying and managing data efficiently. The instructor introduces Hive, a data warehouse software that provides an SQL-like interface for querying data stored in HDFS. Key topics include the creation of databases and tables, the significance of schema on read versus schema on write, and the use of HiveQL for data manipulation. The lecture also covers various data formats, including CSV, ORC, and Parquet, highlighting their performance implications. The instructor engages students with quizzes and practical exercises, reinforcing the concepts discussed. By the end of the lecture, students gain hands-on experience in creating and querying Hive tables, as well as understanding the underlying architecture of Hive and its integration with Hadoop.