Slide deck of Scalable Analytics on the Cloud session delivered by me at "Data Analytics Explained" meetup at Amazon Web Services office in Melbourne on 24 May, 2018. Covers the following topics: 1. What is Machine Learning and the pros and cons of traditional machine learning approaches 2. How big data compliments Machine Learning 3. How the integration of Machine Learning, Big Data and Analytics enable unprecedented value.

1. Memory Speed Big Data Analytics: Alluxio vs Apache IgniteIrfan Elahi - Deloitte 1

2. Data Analytics Explained MeetupIrfan Elahi - Deloitte
• Working as a Senior Consultant in Deloitte (Analytics Service Line)
• Trainer of Deloitte’s Data Science Training
• Speaker at DataWorks Summit, Sydney (2017)
• Premium Udemy Instructor with 17,000+ students from 131 countries
• Technical Reviewer of an upcoming book on Hadoop published by APress
About Me

3. Irfan Elahi - Deloitte Data Analytics Explained Meetup
• The Three Phenomena
• View :: In Isolation -> Conjunction
• Demo and Take-away
Agenda

4. Irfan Elahi - Deloitte Data Analytics Explained Meetup
The drivers behind instrumenting innovation and
provisioning substantial value in capitalizing data-
assets of businesses:
The Three Phenomena…
Intelligence
Scalability
Elasticity

5. Irfan Elahi - Deloitte Data Analytics Explained Meetup
Intelligence

6. Memory Speed Big Data Analytics: Alluxio vs Apache IgniteIrfan Elahi - Deloitte 6
Copyright Deloitte 2015
Intelligence
6
Value
Solution:
Scalability?
Traditional
Approach:
Single Node
In-Memory
Lifecycle:
Acquire -> Transform -> Exploratory Analytics ->
Feature Engineering -> Model Development ->
Evaluation
+ Coverage
+ Strong Visualization
+ Mutability
- Constrained
Resources aka non-
scalable
- Compromise in Data
Locality
- Extensive Engineering
for Productionizing
Tools/Technologies:
• R
• Python (scikit-learn, pandas,
numpy)
• Java (Weka)
• RapidMiner

7. Irfan Elahi - Deloitte Data Analytics Explained Meetup
Scalability

8. Memory Speed Big Data Analytics: Alluxio vs Apache IgniteIrfan Elahi - Deloitte 8
Copyright Deloitte 2015
Scalability
8
8
Value
• Analytics @ Computation Frameworks (Apache Spark, Apache
Flink, Apache Ignite)
• Boutique Analytics Libraries (H2O, DL4J)
• Integration with Traditional tool-set (SparklyR)
• Analytics @ Cloud (Azure ML, AWS ML)
Taxonomy of Scalable Analytics
+ Scalable – Better Intelligence
+ Streamlined Architecture
+ Less engineering overhead
+ Data Locality Optimized
Pros and Cons
- Limited Coverage
- Visualization
- Resourcing for GPUs
Infrastructure Provisioning -> Data Ingestion -> Processing ->
Persistence
Lifecycle:
Time to Value?
Solution:
Cloud?

9. Memory Speed Big Data Analytics: Alluxio vs Apache IgniteIrfan Elahi - Deloitte 9
Copyright Deloitte 2015
Scalability
9
9

10. Irfan Elahi - Deloitte Data Analytics Explained Meetup
Elasticity

11. Irfan Elahi - Deloitte Data Analytics Explained Meetup
01
03
02
04
Rapid Time to Value
Fit for Transient
Loads
Fit For Scalable
Analytics
infrastructure
Pay & Scale As you
Go
ElasticityView :: In Isolation

12. Irfan Elahi - Deloitte Data Analytics Explained Meetup
ElasticityDemo

13. Irfan Elahi - Deloitte Data Analytics Explained Meetup
Elasticity
Intelligence Scalability Elasticity
• For non Big Data problems or rapid prototyping, traditional tool-sets provide better value
• True value for performing analytics at scale with right data lies in leveraging the intelligence,
scalability and elasticity in conjunction
• The conjunction of the three still has challenges and isn’t the answer for every solution, yet
…Questions?

14. Irfan Elahi - Deloitte Data Analytics Explained Meetup
Enrol in my best selling course on Apache Spark for Big Data Analytics at 90% off
price:
https://www.udemy.com/apache-spark-hands-on-course-big-data-
analytics/?couponCode=YOUTUBE2018