Apache Hadoop

Related lectures (29)

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Explores fault tolerance, data safety, and job recovery in distributed computing systems.

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.

Explores the Spark ecosystem, Resilient Distributed Datasets, and the storage layer abstraction in Spark.

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Explores the spark ecosystem in distributed computing and critiques the limitations of MapReduce.

Explores big data challenges, distributed computing with Spark, RDDs, hardware requirements, MapReduce, transformations, and Spark DataFrames.

Explores data-parallel programming with vector processors and SIMD, and introduces MapReduce, Pregel, and TensorFlow.

Explores the challenges of big data processing and introduces Spark as a solution.

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

Introduces the MapReduce programming model for distributed computing, focusing on its vision and under-the-hood mechanisms.