Explores GPUs' architecture, CUDA programming, image processing, and their significance in modern computing, emphasizing early start and correctness in GPU programming.
Delves into challenges of real-time decision-making in data-intensive systems, covering query-driven data sanitization, hardware optimization, and GPU data access.
Explores parallelism in programming, emphasizing trade-offs between programmability and performance, and introduces shared memory parallel programming using OpenMP.
Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.