MapReduce: Execution Models for Distributed Computing
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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 basics of parallel programming, including concurrency, forms of parallelism, synchronization, and programming models like PThreads and OpenMP.
Covers the principles of synchronization in parallel computing, focusing on shared memory synchronization and different methods like locks and barriers.
Explores synchronization principles using locks and barriers, emphasizing efficient hardware-supported implementations and coordination mechanisms like OpenMP.