Architecting Big Data Solutions

How to architect big data solutions by assembling various big data technologies - modules and best practices
4.0 (88 ratings) Instead of using a simple lifetime average, Udemy calculates a
course's star rating by considering a number of different factors
such as the number of ratings, the age of ratings, and the
likelihood of fraudulent ratings.
697 students enrolled
$19
$80
76% off
Take This Course
  • Lectures 42
  • Length 5.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
Wishlisted Wishlist

How taking a course works

Discover

Find online courses made by experts from around the world.

Learn

Take your courses with you and learn anywhere, anytime.

Master

Learn and practice real-world skills and achieve your goals.

About This Course

Published 5/2016 English

Course Description

The Big Data phenomenon is sweeping across the IT landscape. New technologies are born, new ways of analyzing data are created and new business revenue streams are discovered every day. If you are in the IT field, Big data should already be impacting you in some way. 

Building Big Data solutions is radically different from how traditional software solutions were built. You cannot take what you learnt in the traditional data solutions world and apply them verbatim to Big Data solutions. You need to understand the unique problem characteristics that drive Big Data and also become familiar with the unending technology options available to solve them.

This course will show you how Big Data solutions are built by stitching together big data technologies. It explains the modules in a Big Data pipeline, options available for each module and the Advantages, short comings and use cases for each option.

This course is great interview preparation resource for Big Data ! Any one - fresher or experienced should take this course.

Note: This is a theory course. There is no source code/ programming included.

What are the requirements?

  • Familiarity with programming and IT in general

What am I going to get from this course?

  • Understand the differences between Traditional and Big Data Solutions
  • Breakdown a Big Data solution into its modules
  • Look at Technology options for each module
  • Learn the advantages, short comings and use cases for each technology option
  • Architect multiple real life use cases

What is the target audience?

  • Anyone interested in Big Data
  • Software Architects
  • Students in IT
  • Professional preparing for Big Data interviews

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to the course
04:26

Course outline and expectations

About V2 Maestros
Preview
01:39
Course Slides
Article
Section 2: Traditional Data vs Big Data
11:34

How traditional data solutions are built and used

07:57

How Big Data solutions are built and used

08:33

An overview of the current trends in the big data world

Section 3: Big Data Architecture
11:53

An overview of Big Data Solutions

06:22

A template for Big Data architecture - modules and their flow

05:32

Current scenario for technology options in Big Data

08:55

What are the challenges in using Big Data technologies to build today's solutions

Section 4: Data Acquisition Module
09:42

Acquire module - responsibilities, what to architect and best practices

08:23

Using SQL and Flat files  as acquisition options.

08:28

Using HTTP REST and real time streaming for acquiring data

Section 5: Transport Module
09:55

Transport module - responsibilities, what to architect and best practices

11:44

Using SFTP and Apache Sqoop for building Transport modules

10:01

Using Apache Flume and Apache Kafka for building Transport modules

Section 6: Persistence Module
09:58

Persistence module - responsibilities, best practices and what to architect

11:36

Using RDBMS and HDFS to build persistence modules

11:48

Using Cassandra and MongoDB to build persistence layer in a big data solution

08:53

Using Neo4j and ElasticSearch to build persistence modules

Article

Analyze Apache HBase and come up with list of advantages, short comings and use cases.

Section 7: Transformation Module
10:39

Transform module - responsibilities, what to architect and best practices

11:12

Transform options - Use MapReduce and SQL

11:42

Using Apache Spark and commerical ETL products to build transformation modules

Section 8: Reporting Module
08:58

Reporting module - Responsibilities, what to architect and best practices

07:17

Using Apache Impala and Spark SQL to build reporting modules

05:53

Using third party product and Elastic for building reporting modules

Section 9: Advanced Analytics Module
10:01

Advanced Analytics - responsibilities, what to architect and best practices

07:27

Using R and Python for Advanced Analytics

06:33

Using Apache Spark and Commercial products for advanced analytics

Section 10: Big Data Use Cases
06:17

Creating an online data backup solution with Big Data

07:36

Creating a media file store for storing large media files using Big Data

09:50

Acquiring social media data (tweets / posts) and doing real time sentiment analysis as the events happen

10:00

Doing real time credit card fraud detection on website transaction using a big data platform for data storage and predictive analytics

11:28

Building a Big Data platform that acquires log events from a farm of servers and does real time and historical operational analytics.

07:54

Developing predictive relationship models for news articles and using them to recommend items to web site users.

09:47

Building a customer 360 repository by acquiring data from multiple sources and integrating them into a single customer record

08:05

Building a big data platform to acquire car sensor data in real time and predict vehicle equipment failures and generate alarms.

Article

Architect a Spam Classification solution using the techniques learnt in the course

Section 11: Conclusion
Transitioning to Big Data
03:23
01:38

Next Steps

Article

Other courses to checkout and coupons

Students Who Viewed This Course Also Viewed

  • Loading
  • Loading
  • Loading

Instructor Biography

V2 Maestros, Big Data Science / Analytics Experts | 10K+ students

V2 Maestros is dedicated to teaching big data / data science at affordable costs to the world. Our instructors have real world experience practicing big data and data science and delivering business results. Big Data Science is a hot and happening field in the IT industry. Unfortunately, the resources available for learning this skill are hard to find and expensive. We hope to ease this problem by providing quality education at affordable rates, there by building data science talent across the world.

Ready to start learning?
Take This Course