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Related lectures (10)

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

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Hadoop Ecosystem: Architectural Choices & MapReduce Programming

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

Big Data Challenges: Distributed Computing with Spark

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

Scaling up: Spark and Big Data

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

Hadoop: Execution Models

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

MapReduce: Execution Models for Distributed Computing

Introduces the MapReduce programming model for distributed computing, focusing on its vision and under-the-hood mechanisms.

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.

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.

Data Wrangling with Hadoop

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

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