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.
Covers the principles and mechanisms of virtualization, including multiplexing, aggregation, and emulation, with a focus on virtual memory implementation.
Explores memory organization, virtualization, dynamic memory allocation, stack, heap, and memory virtualization techniques like base register and segmentation.
Explores scalable synchronization mechanisms for many-core operating systems, focusing on the challenges of handling data growth and regressions in OS.
