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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 solutions for data center processors, focusing on efficiency, cache issues, branch prediction, and architectural optimizations.
Explores data locality in scheduling decisions for multi-tenant platforms and discusses Hadoop's architecture, execution engine optimizations, and fault tolerance strategies.
Explores gossip efficiency in decentralized systems, covering protocols, interaction needs, and bandwidth optimization, along with search algorithms and optimizations.
