Development
Data Science
Big Data
Architecting Big Data Solutions
3.9 (817 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,544 students enrolled

Architecting Big Data Solutions

How to architect big data solutions by assembling various big data technologies - modules and best practices
3.9 (817 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
4,544 students enrolled
Created by V2 Maestros, LLC
Last updated 1/2017
English
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Architecting Big Data Solutions
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This course includes
  • 5.5 hours on-demand video
  • 4 articles
  • 3 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • 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
Course content
Expand all 42 lectures 05:24:31
+ – Traditional Data vs Big Data
3 lectures 28:04

How traditional data solutions are built and used

Traditional Data Solutions
11:34

How Big Data solutions are built and used

Big Data Solutions
07:57

An overview of the current trends in the big data world

Current trends in Big Data
Preview 08:33
+ – Big Data Architecture
4 lectures 32:42

An overview of Big Data Solutions

Big Data Solutions Overview
11:53

A template for Big Data architecture - modules and their flow

Big Data Architecture Template
06:22

Current scenario for technology options in Big Data

Introduction to Technology options
05:32

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

Challenges with Big Data Technologies
08:55
+ – Data Acquisition Module
3 lectures 26:33

Acquire module - responsibilities, what to architect and best practices

Acquire - Overview
09:42

Using SQL and Flat files  as acquisition options.

Acquire options - SQL and Files
08:23

Using HTTP REST and real time streaming for acquiring data

Acquire options - REST and Streaming
08:28
+ – Transport Module
3 lectures 31:40

Transport module - responsibilities, what to architect and best practices

Transport - Overview
09:55

Using SFTP and Apache Sqoop for building Transport modules

Transport options - SFTP and Apache Sqoop
11:44

Using Apache Flume and Apache Kafka for building Transport modules

Transport Options - Apache Flume and Apache Kafka
10:01
+ – Persistence Module
5 lectures 42:42

Persistence module - responsibilities, best practices and what to architect

Persistence - Overview
09:58

Using RDBMS and HDFS to build persistence modules

Persistence Options - RDBMS and HDFS
11:36

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

Persistence Options - Cassandra and MongoDB
11:48

Using Neo4j and ElasticSearch to build persistence modules

Persistence Options - Neo4j and ElasticSearch
08:53

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

PRACTICE Exercise : Analyze a Product / Technology
00:27
+ – Transformation Module
3 lectures 33:33

Transform module - responsibilities, what to architect and best practices

Transform - Overview
10:39

Transform options - Use MapReduce and SQL

Transform options - MapReduce and SQL
11:12

Using Apache Spark and commerical ETL products to build transformation modules

Transform Options - Apache Spark and ETL products
11:42
+ – Reporting Module
3 lectures 22:08

Reporting module - Responsibilities, what to architect and best practices

Reporting - Overview
08:58

Using Apache Impala and Spark SQL to build reporting modules

Reporting Options - Impala and Spark SQL
07:17

Using third party product and Elastic for building reporting modules

Reporting Options - Third Party Products and Elastic
05:53
+ – Advanced Analytics Module
3 lectures 24:01

Advanced Analytics - responsibilities, what to architect and best practices

Advanced Analytics - Overview
10:01

Using R and Python for Advanced Analytics

Advanced Analytics Options - R and Python
07:27

Using Apache Spark and Commercial products for advanced analytics

Advanced Analytics Options - Apache Spark and Commercial Products
06:33
+ – Big Data Use Cases
9 lectures 01:11:55

Creating an online data backup solution with Big Data

Use Case 1 : Enterprise Data Backup
06:17

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

Use Case 2 : Media File Store
07:36

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

Use Case 3 : Social Media Sentiment Analysis
09:50

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

Use Case 4 : Credit Card Fraud Detection
10:00

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

Use Case 5 : Operational Analytics
11:28

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

Use Case 6 : News Articles Recommendations
07:54

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

Use Case 7 : Customer 360
09:47

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

User Case 8 : IoT - The connected car
08:05

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

PRACTICE Exercise : Architect a Spam Classification solution
00:58
Requirements
  • Familiarity with programming and IT in general
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.

Who this course is for:
  • Anyone interested in Big Data
  • Software Architects
  • Students in IT
  • Professional preparing for Big Data interviews