Hadoop: Execution Models

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Related lectures (32)

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Big Data Challenges: Distributed Computing with Spark

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

Gossip Efficiency: Decentralized Systems

Explores gossip efficiency in decentralized systems, covering protocols, interaction needs, and bandwidth optimization, along with search algorithms and optimizations.

Programming Models: Overview and Examples

Explores programming models for big data processing, including Spark's RDDs and optimizations.

Advanced Spark Optimization

Delves into advanced Spark optimization techniques, emphasizing data partitioning, shuffle operations, and memory management.

Integrating Scalable Data Storage and Map Reduce Processing with Hadoop

Covers the integration of scalable data storage and map reduce processing using Hadoop, including HDFS, Hive, Parquet, ORC, Spark, and HBase.

Data-Parallel Programming: Vector & SIMD Processors

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

Spark Ecosystem: Architectural Choices

Explores the Spark ecosystem's architectural choices, including RDDs and fault tolerance.

Code Optimization: Speeding-up Analyses

Explores techniques to speed up dataflow analyses and discusses the importance of node ordering and post-order traversal.

Spark Data Frames

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Advanced Spark Optimization Techniques: Managing Big Data

Discusses advanced Spark optimization techniques for managing big data efficiently, focusing on parallelization, shuffle operations, and memory management.