Hadoop Ecosystem: Architectural Choices & MapReduce Programming

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

Covers the integration of scalable data storage and map reduce processing using Hadoop, including HDFS, Hive, Parquet, ORC, Spark, and HBase.

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

Explores the Spark ecosystem's architectural choices, including RDDs and fault tolerance.

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

Addresses challenges in data cleaning for analysis, proposing optimizations to reduce processing time.

Covers the basics of Spark DataFrames, their advantages, performance comparison with RDDs, and practical demos.

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

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.