1. Cloud Computing & ANalytics
Nick Guidry, James Hobart, Dylan Larkin, Kayla Potter

2. Agenda
Overview
Benefits
Limitations
Future of Cloud Computing

3. OVERVIEW Shared System Infrastructure Pay-Per-Use Analytics
Service Models

4. OVERVIEW Utilizes Cloud Computing
Use of Data, Statistical Analysis, and Predictive Models
Real-Time Analysis
Visualization

5. Benefits Lower Fixed Costs Increase Margins Scalability Security
Accessibility
Scalability
Security
Benefits

6. Examples of Cloud computing

7. BI Tools in Redshift Analytics in the cloud Using AWS:
Companies can store their data in Amazon simple storage service (S3)
Once stored they can run analytics using standard sql queries in amazon athena or  existing bi tools in amazon redshift
Data in S3
Analytics in Athena
Analysis
BI Tools in Redshift

8. Performance Demands Downtime Security and Privacy Cost Limitations
Data Storage Needs
Performance Demands
Downtime
Security and Privacy
Cost
Companies must determine exactly how big is their big data, as not renting enough space from a cloud service provider could end up giving a company significant infrastructure issues and perhaps not allow them to use their big data analysis as intended or to its full capabilities
Determining the computing power of a cloud drive is of importance, as underestimating the demands placed on it could slow service and make the cloud less effective
If data being processed and accessed on cloud drives seems random and from vastly different fields, it could strain the magnetic disks in the cloud servers and cause them not to operate at their full potential when working with large data sets
Diversifying data to multiple cloud servers as not to strain individual servers can help clouds operate more like traditional networks, keeping them competitive
Some clouds cannot host of analyst certain sets of data regardless of their size or capability given the scope of some data sets
Ex: SaaS – you only have access if you pay your dues, never fully having the software on the harddrive of their own computers. Limiting if they do not have access to the internet or wish to work offline
Can be costly if buying a subscriptionfor a long period of time
Fully dependent on internet connection, subject to service outages, can fail just like any other hardware --> need SLA from your provider
Access management, who is supposed to have access to each resource and service, limit data access based on user context,  risk based approach, 
Also vulerabile to attacks, everything is potentially accessible from the internet
Costly on a small scale and for short term projects although it reduces staff and hardware costs
The cloud has immense potential for many businesses. As platforms continue to mature and the ecnomies of scale begin to grow, costs will continue to fall and reliability and security standards will improve. 

9. Future of Cloud Computing
commoditized
Innovation  will focus on usability and application development
Growth for big players like AWS and Azure 
Breakthroughs in AI
Massive pools of data + unlimited computing resources
Personalized Medical Treatment
World class computing infrastructure for startups
Algorithms have been around for decades - combination of these algorithms, access for cheap ways to store information, process and query data (to train these algorithms), and access to specialized compute infrastructure (e.g., GPU infrastructure, custom ASICs) that can run these algorithms efficiently have spurred 
AWS is investing in all layers of the stack from core deep learning frameworks (such as Apache MXNet, Caffe, Caffe2, TensorFlow), machine learning platforms, AI application services (such as Amazon Lex, Amazon Polly and Amazon Rekognition). 
PMT - Research institutions, hospitals, genomics labs

10. Sources