Fault Tolerance and Recovery: Data Safety in Distributed Computing

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

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.

Introduction to Spark Runtime Architecture

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

Data Wrangling with Hadoop

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

Hadoop Ecosystem: Architectural Choices & MapReduce Programming

Explores the Hadoop ecosystem's architecture and MapReduce programming model, emphasizing strengths and limitations.

Execution Models for Distributed Computing - 2nd generation

Explores the 2nd generation of execution models for distributed computing, focusing on Spark and Resilient Distributed Datasets (RDDs).

Introduction to Spark runtime architecture

Introduces Apache Spark, covering its key features, history, RDDs, architecture, and distributed computing framework.

Data formats and data wrangling with Hadoop

Explores Apache Hive for data warehousing, data formats, and partitioning, with practical exercises in querying and connecting to Hive.

Data Wrangling with Hadoop: Storage Formats and Hive

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