Execution Models for Distributed Computing - 2nd generation

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Related lectures (32)

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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.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

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.

Big Data Best Practices and Guidelines

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

Big Data Challenges: Distributed Computing with Spark

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

Data Wrangling with Hadoop

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.

Introduction to Spark Runtime Architecture

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

Scaling up: Spark and Big Data

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

Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

General-Purpose Distributed Execution System

Explores the design of a general-purpose distributed execution system, covering challenges, specialized frameworks, decentralized control logic, and high-performance shuffle.