Hadoop Ecosystem: Architectural Choices & MapReduce Programming

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Data-Parallel Programming: Vector & SIMD Processors

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

Advanced Spark Optimization

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

Introduction to Computer Science: Three Domains

Covers scientific computing, process automation, and data management in computer science applications across different sectors.

Introduction to Spark Runtime Architecture

Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

Code Optimization: Speeding-up Analyses

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

Data Wrangling with Hadoop: Storage Formats and Hive

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

Data Wrangling Techniques: HBase and Hive Integration

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

Dataflow Analysis: Optimization

Explores dataflow analysis for optimization, including equations solving, live variables, reaching definitions, and very busy expressions.

Spark Storage Layer

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

Introduction to Spark Runtime Architecture

Introduces Apache Spark, covering its architecture, RDDs, transformations, actions, fault tolerance, deployment options, and practical exercises in Jupyter notebooks.