Architecting Big Data Solutions

How to architect big data solutions by assembling various big data technologies - modules and best practices
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  • 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
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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

Who 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.


Section 1: Introduction to the course

Course outline and expectations

About V2 Maestros
Course Slides
Section 2: Traditional Data vs Big Data

How traditional data solutions are built and used


How Big Data solutions are built and used


An overview of the current trends in the big data world

Section 3: Big Data Architecture

An overview of Big Data Solutions


A template for Big Data architecture - modules and their flow


Current scenario for technology options in Big Data


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

Section 4: Data Acquisition Module

Acquire module - responsibilities, what to architect and best practices


Using SQL and Flat files  as acquisition options.


Using HTTP REST and real time streaming for acquiring data

Section 5: Transport Module

Transport module - responsibilities, what to architect and best practices


Using SFTP and Apache Sqoop for building Transport modules


Using Apache Flume and Apache Kafka for building Transport modules

Section 6: Persistence Module

Persistence module - responsibilities, best practices and what to architect


Using RDBMS and HDFS to build persistence modules


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


Using Neo4j and ElasticSearch to build persistence modules


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

Section 7: Transformation Module

Transform module - responsibilities, what to architect and best practices


Transform options - Use MapReduce and SQL


Using Apache Spark and commerical ETL products to build transformation modules

Section 8: Reporting Module

Reporting module - Responsibilities, what to architect and best practices


Using Apache Impala and Spark SQL to build reporting modules


Using third party product and Elastic for building reporting modules

Section 9: Advanced Analytics Module

Advanced Analytics - responsibilities, what to architect and best practices


Using R and Python for Advanced Analytics


Using Apache Spark and Commercial products for advanced analytics

Section 10: Big Data Use Cases

Creating an online data backup solution with Big Data


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


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


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


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


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


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


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


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

Section 11: Conclusion
Transitioning to Big Data

Next Steps


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Instructor Biography

V2 Maestros, LLC, 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.

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