Explores Hadoop's execution models, fault tolerance, data locality, and scheduling, highlighting the limitations of MapReduce and alternative distributed processing frameworks.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.
Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.
