Hadoop: Execution Models

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Related lectures (32)

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Scaling up: Spark and Big Data

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

VLIW Architectures: Compilers and Instruction Level Parallelism

Covers VLIW architectures and compilers, focusing on instruction level parallelism and optimization techniques.

Dataflow: Execution Models for Distributed Computing

Explores the data flow model for distributed computing using RDDs in Spark.

Decentralized Systems Engineering: Gossip Efficiency

Explores gossip efficiency in decentralized systems, focusing on minimizing duplication and reducing traffic through improved gossiping techniques.

Dataflow Analysis: Optimization

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

Data Wrangling with Hadoop: Advanced Techniques

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

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.

Control structures in programming

Explores control structures in programming, emphasizing logical operators, branching, loops, and potential pitfalls of break and continue statements.

Dataflow Analysis: Translation Inefficiencies and Optimizations

Explores translation inefficiencies, optimizations, hoisting functions, closure conversion, and dataflow analysis concepts like available expressions and live variables.

Interaction Model Design Explanation

Explains the design model for managing interactions in small games with actors.