Big Data Analytics & Architecture Course Overview

17 Questions

What is the main course objective of Big Data Analytics & Architecture?

To provide an overview of big data analytics

What is one of the learning outcomes of the course?

Mining and processing of Big Data

Which tool is NOT mentioned in relation to managing and analyzing big data in the course objective?

MySQL

What aspect of Hadoop is emphasized in the course objective?

Mining and processing Big Data

What does the course aim to prepare students for?

Developing sample projects in Hadoop

What is the role of Apache Spark SQL in the Spark Unified stack?

To process structured data using SQL queries

Which component of the Spark Unified stack is responsible for handling distributed datasets?

RDD

What is a common use case for Apache Kafka?

Real-time data processing and streaming

What is a key feature of Apache Spark's MLib (Machine Learning Library)?

Built-in support for popular machine learning algorithms

Which data file format is commonly used for storing semi-structured data?

JSON

What is a distinguishing characteristic of NoSQL databases compared to traditional relational databases?

Schema flexibility to handle diverse data types

What is the focus of Module I in the course?

Introduction to NoSQL databases

Which topic is covered in Module III of the course?

Exploring classes and objects

In Module IV, what is one of the purposes of using MapReduce?

Finding top-N records

What is the role of a Resilient Distributed Dataset (RDD) in Apache Spark?

Handling distributed data in memory

Which programming languages are commonly used with Apache Spark according to the course content?

Scala and Python

What is an essential topic covered in Module II of the course?

Primitive Types and Vars vs Vals

Study Notes

Course Overview

The Big Data Analytics & Architecture course provides an overview of the growing field of big data analytics.
The course introduces tools required to manage and analyze big data like Hadoop, NoSql, and MapReduce.
It explains the importance of Bigdata, Spark, and strengthens understanding of basic concepts of Spark and Scala.

Course Objectives

Upon completion of the course, students will be able to develop an understanding of the complete open-source Hadoop ecosystem and its near-term future direction.
Students will understand the MapReduce model v1 and review Java code.
Students will develop an understanding of mining big data and processing data streams.

Course Contents

Module I: Introduction to BigData

Introduces NoSQL databases for big data storage applications.
Covers introduction to Scala and Spark.
Includes Apache Storm, implementing data ingress and egress.
Covers understanding the basics of the language, setting up the environment, and writing the first "Hello World" program.

Module II: Scala Basics

Covers Hello World, primitive types, and type inference.
Introduces vars vs vals, lazy vals, and methods.

Module III: Understanding Decision Making

Covers loops, literals, and the 'yield' keyword.
Introduces OOP concepts: classes, objects, inheritance, operators, abstract classes, constructors, case classes, and polymorphism.

Module IV: Processing Engine

Covers MapReduce architecture, mapper in MapReduce, and combiners.
Explains streaming MapReduce with a real-life example.
Covers how to find top-N records using MapReduce.

Module V: Spark Core

Explains the nature and purpose of Apache Spark in the Hadoop ecosystem.
Describes the architecture and components of the Apache Spark unified stack.
Explains the principles of Apache Spark programming and the role of RDD.
Covers Apache Spark libraries, streaming, SQL, MLib, and Graphx.

Module VI: Components of Spark Unified Stack

Covers RDD, word count using Scala, and introduction to queuing systems like Kafka.
Explains the need for Kafka, its features, concepts, architecture, and components.

Lab Experiments

Covers installing the machine on a system with recommended configuration.
Explains the need for a VM of a pseudo-distributed system.
Covers implementing hello world in Scala programming, running basic MapReduce jobs, and conditional statements in Scala.
Covers implementing polymorphism and constructors in Scala and working with NoSQL databases.
Explains the working principle of RDD and writes the code of a word count program using Apache Spark.

Learn about the basics of big data analytics, including tools like Hadoop, NoSql MapReduce, and Spark. Understand the importance of big data and strengthen your knowledge of concepts like spark and Scala. Gain practical experience by working on a sample project in Hadoop.

Make Your Own Quizzes and Flashcards

Convert your notes into interactive study material.