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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.
Explores challenges and innovations in database systems, emphasizing the need for efficient data management and adapting to modern hardware advancements.
Covers pipelining in computer architecture, focusing on its role in enhancing performance through instruction-level parallelism and addressing associated challenges.
Explores challenges and solutions for data center processors, focusing on efficiency, cache issues, branch prediction, and architectural optimizations.
