Lecture
This lecture provides an in-depth overview of data wrangling techniques using HBase and Hive. It begins with a general introduction to data science tools such as Python, Jupyter notebooks, and Spark. The instructor discusses the differences between HDFS and Hive, emphasizing the strengths and weaknesses of each in handling big data. The lecture covers the architecture of HBase, including its column-oriented data model and the importance of key design for efficient data retrieval. The integration of Hive with HBase is also explored, highlighting how Hive can be used to perform queries on data stored in HBase. The session includes practical exercises on using Hive with JSON data and user-defined functions (UDFs). The lecture concludes with a summary of the best practices for using HDFS, Hive, and HBase, providing students with a comprehensive understanding of how to effectively manage and query large datasets in a distributed environment.