courseraПрограммирование

Big Data Technologies

Курс от Illinois Tech

Средний≈ 98 чАнглийский

О курсе

Big data is the area of informatics focusing on datasets whose size is beyond the ability of typical database and other software tools to capture, store, analyze and manage. This course provides a rapid immersion into the area of big data and the technologies which have recently emerged to manage it. We start with an introduction to the characteristics of big data and an overview of the associated technology landscape and continue with an in depth exploration of Hadoop, the leading open source framework for big data processing. Here the focus is on the most important Hadoop components such as Hive, Pig, stream processing and Spark as well as architectural patterns for applying these components. We continue with an exploration of the range of specialized (NoSQL) database systems architected to address the challenges of managing large volumes of data. Overall the objective is to develop a sense of how to make sound decisions in the adoption and use of these technologies as well as economically deploy them on modern cloud computing infrastructure.

Программа курса

9 модулей · 248 учебных материалов

01Module 1: Big Data Concepts35 материалов

Course Welcome

Course OverviewВидео Instructor IntroductionВидео SyllabusЧтение Meet and Greet DiscussionОбсуждение

Module 1 Introduction

Учитесь у экспертов

Yousef Elmehdwi

Associate Teaching Professor of CS

Big Data Technologies

О курсе

Навыки, которые вы освоите

Программа курса

Course Welcome

Module 1 Introduction

Учитесь у экспертов

Yousef Elmehdwi

Lesson 1: From Data to Value

Lesson 2: Big Data Overview

Lesson 3: Confounding Factors

Lesson 4: Big Data Challenges

Lesson 5: Big Data Benefits

Lesson 6: Big Data Technology

Lesson 7: Generic Distributed Storage Systems and Execution Engines

Module 1 Summative Assessment

Module 1 Summary

Module 2 Introduction

Lesson 1: Hadoop

Lesson 2: Hadoop Distributed File System (HDFS) Overview

Lesson 3: Using Hadoop Distributed File System

Lesson 4: Cloud Object Storage for Big Data

Lesson 5: Yet Another Resource Negotiator (YARN)

Module 2 Summative Assessment

Module 2 Summary

Module 3 Introduction

Lesson 1: The Path to MapReduce

Lesson 2: MapReduce Overview

Lesson 3: MapReduce Concepts

Lesson 4: MapReduce Examples

Lesson 5: MapReduce Programming

Lesson 6: MapReduce Optimization

Module 3 Summative Assessment

Module 3 Summary

Module 4 Introduction

Lesson 1: Spark Overview

Lesson 2: Spark Components

Lesson 3: Concepts

Lesson 4: Creating Spark DataFrames

Lesson 5: Defining Spark Schemas

Module 4 Summative Assessment

Module 4 Summary

Module 5 Introduction

Lesson 1: Transformation - Rows

Lesson 2: Transformations - Columns

Lesson 3: Transformations - Join

Lesson 4: Transformations - Aggregations

Lesson 5: Transformations - Working with Null Values

Lesson 6: Transformations - Spark SQL

Lesson 7: Transformations - Caching

Lesson 8: Actions

Lesson 9: Actions - Writing Data

Module 5 Summative Assessment

Module 5 Summary

Module 6 Introduction

Lesson 1: Stream Ingestion and Processing (Part 1)

Lesson 2: Stream Ingestion and Processing (Part 2)

Lesson 3: Analytic Cluster Pattern

Lesson 4: Data Lake Pattern

Lesson 5: Lambda Architecture

Module 6 Summative Assessment

Module 6 Summary

Module 7 Introduction

Lesson 1: Using Databases for Big Data Storage

Lesson 2: NoSQL Database Concepts (Part 1)

Lesson 3: NoSQL Database Concepts (Part 2)

Lesson 4: NoSQL Database Classifications (Part 1)

Lesson 5: NoSQL Database Classifications (Part 2)

Module 7 Summative Assessment

Module 7 Summary

Module 8 Introduction

Lesson 1: HB Base

Lesson 2: Cassandra

Lesson 3: Mongo DB

Module 8 Summative Assessment

Module 8 Summary

Summative Course Assessment