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Architecting Big Data Solutions

How to architect big data solutions by assembling various big data technologies - modules and best practices
Instructor:
V2 Maestros, LLC
3,701 students enrolled
English [Auto-generated]
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

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.

Introduction to the course

1
Introduction

Course outline and expectations

2
About V2 Maestros
3
Course Slides

Traditional Data vs Big Data

1
Traditional Data Solutions

How traditional data solutions are built and used

2
Big Data Solutions

How Big Data solutions are built and used

3
Current trends in Big Data

An overview of the current trends in the big data world

Big Data Architecture

1
Big Data Solutions Overview

An overview of Big Data Solutions

2
Big Data Architecture Template

A template for Big Data architecture - modules and their flow

3
Introduction to Technology options

Current scenario for technology options in Big Data

4
Challenges with Big Data Technologies

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

Data Acquisition Module

1
Acquire - Overview

Acquire module - responsibilities, what to architect and best practices

2
Acquire options - SQL and Files

Using SQL and Flat files  as acquisition options.

3
Acquire options - REST and Streaming

Using HTTP REST and real time streaming for acquiring data

Transport Module

1
Transport - Overview

Transport module - responsibilities, what to architect and best practices

2
Transport options - SFTP and Apache Sqoop

Using SFTP and Apache Sqoop for building Transport modules

3
Transport Options - Apache Flume and Apache Kafka

Using Apache Flume and Apache Kafka for building Transport modules

Persistence Module

1
Persistence - Overview

Persistence module - responsibilities, best practices and what to architect

2
Persistence Options - RDBMS and HDFS

Using RDBMS and HDFS to build persistence modules

3
Persistence Options - Cassandra and MongoDB

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

4
Persistence Options - Neo4j and ElasticSearch

Using Neo4j and ElasticSearch to build persistence modules

5
PRACTICE Exercise : Analyze a Product / Technology

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

Transformation Module

1
Transform - Overview

Transform module - responsibilities, what to architect and best practices

2
Transform options - MapReduce and SQL

Transform options - Use MapReduce and SQL

3
Transform Options - Apache Spark and ETL products

Using Apache Spark and commerical ETL products to build transformation modules

Reporting Module

1
Reporting - Overview

Reporting module - Responsibilities, what to architect and best practices

2
Reporting Options - Impala and Spark SQL

Using Apache Impala and Spark SQL to build reporting modules

3
Reporting Options - Third Party Products and Elastic

Using third party product and Elastic for building reporting modules

Advanced Analytics Module

1
Advanced Analytics - Overview

Advanced Analytics - responsibilities, what to architect and best practices

2
Advanced Analytics Options - R and Python

Using R and Python for Advanced Analytics

3
Advanced Analytics Options - Apache Spark and Commercial Products

Using Apache Spark and Commercial products for advanced analytics

Big Data Use Cases

1
Use Case 1 : Enterprise Data Backup

Creating an online data backup solution with Big Data

2
Use Case 2 : Media File Store

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

3
Use Case 3 : Social Media Sentiment Analysis

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

4
Use Case 4 : Credit Card Fraud Detection

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

5
Use Case 5 : Operational Analytics

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

6
Use Case 6 : News Articles Recommendations

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

7
Use Case 7 : Customer 360

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

8
User Case 8 : IoT - The connected car

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

9
PRACTICE Exercise : Architect a Spam Classification solution

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

Conclusion

1
Transitioning to Big Data
2
Closing Remarks

Next Steps

3
BONUS Lecture : Other courses you should check out

Other courses to checkout and coupons

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Includes

5 hours on-demand video
4 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion