Getting Started with Big Data Course Overview

The "Getting Started with Big Data" course is a comprehensive program designed to introduce learners to the expansive world of big data analytics. It aims to provide a foundation in understanding and utilizing big data tools and methodologies, specifically focusing on Hadoop and its ecosystem, as well as Apache Spark and Kafka.

Beginning with Module 1, participants will get a Big Data Overview that covers the essential Five Vs of Big Data and dives into the relationship between Big Data and Hadoop. The module further explores the Components of the Hadoop Ecosystem and introduces the basics of Big Data Analytics.

Module 2 shifts focus to HDFS (Hadoop Distributed File System) and Map Reduce, key components for big data storage and distributed processing. The lessons will clarify the Mapping and Reducing stages and familiarize learners with terms like Output Format, Partitioners, Combiners, and the Shuffle and Sort process.

PySpark Foundation is the core of Module 3, where learners will understand how to configure Spark and manipulate Resilient Distributed Datasets (RDDs), which are crucial for Aggregating Data in big data processing.

Module 4 contrasts Spark SQL with Hadoop Hive, guiding students through practical applications using the Spark SQL Query Language.

In Module 5, the course takes a leap into Machine Learning with Spark ML, covering various algorithms such as Linear Regression, Logistic Regression, and Random Forest.

Finally, Module 6 introduces the streaming platform Kafka, outlining its architecture, workflow, and cluster configuration.

Overall, this course will empower learners with the knowledge and practical skills needed to navigate the big data landscape, making them valuable assets in fields that require data-driven decision-making.