Lecture
This lecture provides a comprehensive overview of big data, covering topics such as data science tools like Python, collaborative data science, data wrangling with Hadoop, Spark runtime architecture, and data lake ecosystems. It delves into the challenges of big data, the CAP theorem of distributed data stores, and the decision between batch and stream processing. The lecture also explores technologies like Hadoop Distributed File System (HDFS), Hive data warehouse, and popular storage formats like Parquet and ORC. Additionally, it discusses the MapReduce architecture with YARN, typical big data architectures, and the evolution of the big data landscape.