Spark Storage Layer

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Related lectures (32)

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Data Wrangling with Hive: Managing Big Data Efficiently

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

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

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.

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.

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.

Introduction to Spark Runtime Architecture

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

Hadoop: Execution Models

Explores Hadoop's execution models, fault tolerance, data locality, and scheduling, highlighting the limitations of MapReduce and alternative distributed processing frameworks.

Data Wrangling with Hadoop: Advanced Techniques

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

Hadoop Ecosystem: Architectural Choices & MapReduce Programming

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

Big Data: Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, typical architecture, challenges, and technologies used to address them.