Data analytics for full stack developer

## Module 1: The Foundation of Big Data
Think of this module as learning the fundamental laws for large-scale data.
While a single computer can handle gigabytes, the industry now operates in petabytes.
Here, you’ll learn how the Hadoop ecosystem first solved this problem.
It would help in understanding of federated system to process the data with community resource.

You’ll explore HDFS (Hadoop Distributed File System), the blueprint for distributed storage, and YARN,
the resource manager that made distributed computing possible. While MapReduce was the original processing engine,
understanding its concept is key to grasping how modern tools like Spark process data in parallel.
This module gives you the essential “why” behind every modern Big Data platform.

Topics covered

• • • What is Big Data
    • Why Big data is important in Industry with use cases and different roles
    • Hadoop set up in local machine
    • Explaining Hadoop YARN key components
    • HDFS- Importance of distributed file system
    • HDFS commands and usage of Distributed file system
    • Map Reduce Concept and its applicability in Big data
    • Key components of Map Reduce programme
    • Concept of Job
    • Map Reduce programme implementation

## Module 2: Structuring and Analyzing Data at Scale
Once you know how to store data, the next step is to make it useful. This module brings you into the world of
modern data engineering and analytics. You’ll start with the Data Lakehouse, a cutting-edge architecture that combines the best of data lakes and data warehouses.

You’ll get hands-on with Hive, the tool that first brought familiar SQL queries to the Big Data world. We’ll also cover timeless, critical concepts like OLTP vs. OLAP (the difference between operational databases and analytical ones) and the evolution of data pipelines from ETL to ELT. Mastering these topics will give you the practical skills to build systems that turn raw data into valuable business insights.