Amazon Web Services proporciona una amplia gama de servicios que le ayudarán a crear e implementar aplicaciones de análisis de big data de forma rápida y sencilla. AWS ofrece un acceso rápido a recursos de TI económicos y flexibles, algo que permitirá escalar prácticamente cualquier aplicación de big data con rapidez, incluidos almacenamiento de datos, análisis de clics, detección de elementos fraudulentos, motores de recomendación, proceso ETL impulsado por eventos, informática sin servidor y procesamiento del Internet de las cosas. https://aws.amazon.com/es/big-data/

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Renato Da Paz
Business Development Manager - LATAM
Big Data and Analytics:
Innovating at the Speed of Light

2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Big Data

3. Generation Collection &
Storage
Analytics &
Computation
Collaboration &
Sharing

4. Generation Collection &
Storage
Analytics &
Computation
Collaboration &
Sharing

5. 95% of the 1.2 zettabytes
of data in the digital
universe is unstructured
70% of of this is user-
generated content
Unstructured data growth
explosive, with estimates
of compound annual
growth (CAGR) at 62%
from 2008 – 2012.
Source: IDC
GB TB
PB
ZB
EB
Big Data: Unconstrained data growth

6. Lower cost,
higher throughput
Generation Collection &
Storage
Analytics &
Computation
Collaboration &
Sharing

7. Customer segmentation
Marketing spend optimization
Financial modeling & forecasting
Ad targeting & real time bidding
Clickstream analysis
Fraud detection
Use Cases

8. Visits, views, clicks, purchases
Source, device, location, time
Latency, throughput, uptime
Likes, shares, friends, follows
Price, frequency
Metrics

9. Relational
NoSQL
Web servers
Mobile phones
Tablets
3rd party feeds
Sources

10. Structured
Unstructured
Text
Binary
Near Real-time
Batched
Formats

11. Reporting
Dashboards
Sentiment
Clustering
Machine Learning
Optimization
Analysis

12. Lower cost,
higher throughput
Highly
constrained
Generation Collection &
Storage
Analytics &
Computation
Collaboration &
Sharing

13. Gartner: User Survey Analysis: Key Trends Shaping the Future of Data Center Infrastructure Through 2011
IDC: Worldwide Business Analytics Software 2012–2016 Forecast and 2011 Vendor Shares
Available for analysis
Generated data
Data volume - Gap
1990 2000 2010 2020

14. Elastic and highly scalable
No upfront capital expense
Only pay for what you use
+
+
Available on-demand
+
=
Remove constraints

15. Accelerated
Generation Collection &
Storage
Analytics &
Computation
Collaboration &
Sharing

16. Big Data
Technologies and techniques for working
productively with data, at any scale.

17. Big data and AWS Cloud computing
Big data Cloud computing
Variety, volume, and velocity
requiring new tools
Variety of compute, storage,
and networking options

18. Big data and AWS Cloud computing
Potentially massive datasets Massive, virtually unlimited capacity
Big data Cloud computing

19. Big data and AWS Cloud computing
Iterative, experimental style of
data manipulation and analysis
Iterative, experimental style of
infrastructure deployment/usage
Big data Cloud computing

20. Big data and AWS Cloud computing
Frequently not steady-state
workload; peaks and valleys
At its most efficient with highly
variable workloads
Big data Cloud computing

21. Big data and AWS Cloud computing
Absolute performance not as
critical as “time to results”;
shared resources are a
bottleneck
Parallel compute projects allow
each workgroup to have more
autonomy, get faster results
Big data Cloud computing

22. Ease of useLower costs

23. Only pay for what you use
No capital investment
Pay as you goLower costs

24. Programmable
Integrate with existing tools
Zero administration
Easy to configure
Ease of use

25. One tool to
rule them all

26. Use the right tools
Amazon
S3
Amazon
Kinesis
Amazon
DynamoDB
Amazon
Redshift
Amazon
Elastic
MapReduce

27. Store anything
Object storage
Scalable
99.999999999% durability
Amazon S3

28. Real-time processing
High throughput; elastic
Easy to use
EMR, S3, Redshift, DynamoDB
Integrations
Amazon Kinesis

29. NoSQL Database
Seamless scalability
Zero administration
Single digit millisecond latency
Amazon
DynamoDB

30. Relational data warehouse
Massively parallel
Petabyte scale
Fully managed
$1,000/TB/year
Amazon
Redshift

31. Hadoop/HDFS clusters
Hive, Pig, Impala, HBase
Easy to use; fully managed
On-demand and spot pricing
Tight integration with S3,
DynamoDB, and Kinesis
Amazon
Elastic
MapReduce

32. Automatically Discovers Data
Catalog makes data searchable
Catalog contains tables & jobs
Automatically generates ETL
Scheduled or event-based jobs
Amazon
Glue

33. Visualization Tool
Easy to Use
Fast and Scalable
Small investment
Amazon
QuickSight

34. Optimal Combinations of Interoperable Services
Amazon Redshift Amazon Elastic
MapReduce
Data Warehouse Semi-structured
Amazon
Glacier
Amazon Simple
Storage Service
Data Storage Archive
Amazon
DynamoDB
Amazon
Machine
Learning
Amazon Kinesis
NoSQL Predictive Models
Other
Services
Streaming

35. Sample Reference Architecture: Data Lake
Kinesis Firehose
Athena
Query Service

36. Media streaming
Free steak campaign
Disaster recovery
Web site & media sharing
Facebook app
Ground campaign
SAP & SharePoint
Marketing web site
Social Media Monitoring
Consumer social app
IT operations
Mars exploration ops
Interactive TV apps
Consumer social app
Facebook page
Securities Trading Data Archiving
Financial markets analytics
Web and mobile apps
Big data analytics
Digital media
Ticket pricing optimization
Streaming webcasts
Mobile analytics
Consumer social app
Core IT and media

37. 1.2TB/Day logs
30TB /Day data
250 Hadoop Jobs
75Billion transactions/Day
5 Petabytes of Data
A few AWS customer on Big Data / Data Lakes
25 PB Data Warehouse
on Amazon S3
> 1PB read each day

38. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Helps to Run the Amazon Business
• Most Comprehensive Set of Cleansed and Curated Business Data
• Feeds Many Downstream Systems and Processes
• Batch Processing, Reporting and Ad Hoc
• 500k+ Data Loads/Transformations Each Day
• 200k+ Queries/Extracts Each Day
• 20k+ Active Tables
• 10B++ Rows Loaded Daily
Our Data is Big!
• Core Data Set: 5+PB of Compressed Data (primarily limited by Legacy Technology)
• Total Storage (Multiple Systems): 35+ PB compressed
Significant and Increasing Use of Redshift and EMR
• 1000’s of Redshift and EMR Systems, Range in size from:
• Individual Contributor - Project Based, to
• Running Multi-Billion Dollar Business inside Amazon
The Amazon Enterprise Data Warehouse

39. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Artificial Intelligence and
Machine Learning

40. Breakthrough
advances
Optimization and
automation
AI and ML enable innovation at scale…
New features
for existing products
“After decades of false starts, artificial intelligence is on the verge of a
breakthrough, with the latest progress propelled by machine learning.”
McKinsey Global Institute, Artificial Intelligence The Next Digital Frontier? June 2017

41. The potential impact of AI/ML is enterprise-wide
Compliance,
Surveillance, and
Fraud Detection
Pricing and
Product
Recommendation
Document
Processing
Trading Customer
Experience
• Credit card/account fraud
detection
• Anti-money laundering/
Sanctions
• Investigations optimization
• Sales practices/
transaction surveillance
• Compliance processes
optimization
• Regulatory mapping
• Enhanced customer
service through voice
services and chatbots
• Call center
optimization
• Personal financial
management
• Loan/Insurance
underwriting
• Sales/recommendations of
financial products
• Credit assessments
• Contract ingestion and
analytics
• Financial information
extraction
• Common financial
instrument taxonomy
• Corporate actions
• Portfolio management/
robo-advising
• Algorithmic trading
• Sentiment/news analysis
• Geospatial image analysis
• Predictive grid computing
capacity management
AI/ML use cases are gaining traction in Financial Services

42. What is preventing the industry from moving ahead?
AI/ML
expertise is
rare
Building and
scaling AI/ML
technology is hard
Deploying and operating
models in production is
time-consuming and
expensive
A lack of cost-effective,
easy-to-use, and
scalable AI/ML services

43. AWS offers a range of solutions to make AI/ML more accessible
PollyLex Rekognition
Deep Learning FrameworksMachine Learning PlatformsAmazon AI/ML Services
Usability/simplicity:
leverages AWS AI/ML expertise
Greater control:
customer-specific models
Amazon ML
Spark & EMR
Kinesis
Batch
ECS
Customization of
offerings at scale
More personal and
efficient customer
interactions
Operational
efficiencies
Novel investment/
trading opportunities
Benefits for Financial Services Institutions
and others...

44. Our deep experience with AI/ML differentiates our services
Product
recommendation
engine
Robot-enabled
fulfillment
centers
New
product
categories
Amazon has invested in AI/ML since our
inception, and we share our knowledge and
capabilities with our customers
20171995
Natural language
processing-supported
contact centers
ML-driven supply
chain and
capacity planning
Checkout-free
shopping
using deep learning

46. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

50. Alexa...
25,000+ skills

51. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The Alexa Family

53. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
A Little About Ally…
…A digitally disruptive, diversified financial services firm
with a full suite of financial products and services
including banking, auto finance, and mortgage offerings.
Beyond its services, Ally is known for its unique culture,
straight forward approach and customer-centric business
philosophy.

54. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Ally Business Lines
• Full-spectrum provider
• New, used, leasing
• Floor plan and
commercial services
• Dealer online services
• Auto-focused insurance
business
• Direct banking platform
• Deposit products:
savings, checking, CDs,
IRAs
• Mobile banking
• Credit card
• Mortgage
• Financing for mid-
market companies in
technology, healthcare,
retail, and automotive
• Digital portfolio
management platform
• Ally Invest
Auto Finance Ally Bank Corporate Finance
Wealth Management
& Online Brokerage

55. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The Design Journey

56. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The Ally Skill
"ALEXA, OPEN ALLY”
“GET MY BALANCE” “HOW MUCH DID I SPEND LAST MONDAY?”
“ALEXA, TRANSFER $10 FROM MY
ALLY BANK [PRODUCT NAME] ACCOUNT
TO MY [ELIGIBLE EXTERNAL ACCOUNT]”
TO VERIFY IT'S YOU, TELL ME YOUR 6-
DIGIT PASSCODE
“WHAT'S TODAY’S RATE FOR A 12 MONTH HIGH YIELD CD”
“CONVERT $300 INTO [CURRENCY]”“ALEXA, ASK ALLY FOR HELP.”

59. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

60. AWS Machine Learning Stack
Frameworks &
Infrastructure
AWS Deep Learning AMI
GPU
(P3 Instances)
Mobile
CPU
(C5 Instances)
IoT
(Greengrass)
Vision:
Rekognition Image
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex Translate
Comprehend
Apache
MXNet PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow Gluon
Application
Services
Platform
Services
Amazon Machine
Learning
Mechanical
Turk
Spark &
EMR
Amazon
SageMaker
AWS
DeepLens

61. And today, enterprises across industries run AI/ML on AWS

62. Fraud.net is running AI/ML on AWS to predict financial crime
“Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns.
We can see correlations we wouldn’t have been able to see otherwise and answer questions it would
have taken us way too long to answer ourselves.
”
Fraud.net is the world’s leading
crowdsourced fraud prevention
platform, aggregating and
analyzing large amounts of
fraud data from thousands of
online merchants in real time.
The platform protects more
than 2 percent of all U.S. e-
commerce.
- Oliver Clark, CTO, Fraud.net
• To address its scalability needs, Fraud.net chose AWS to host its customer
platform, relying on services including DynamoDB, Lambda, S3, and Redshift
• Recently, Fraud.net started using Amazon Machine Learning, which helps its
developers build models and enables the use of APIs to get predictions for
applications without having to deploy prediction generation code
• Fraud.net can now easily launch and train new machine-learning models to target
evolving forms of fraud
• Using AWS, Fraud.net can maintain its fast application response times of under
200 milliseconds and save its customers about $1 million a week through fraud
detection and prevention

63. BuildFax uses Amazon ML to help insurers avoid losses
“Amazon Machine Learning democratizes the process of building predictive models. It’s easy and fast
to use and has machine-learning best practices encapsulated in the product, which lets us deliver
results significantly faster than in the past.
”
BuildFax aggregates dispersed
building permit data from across
the United States and provides it
to other businesses, especially
insurance companies, and
economic analysts. The
company also tracks trends like
housing remodels and new
commercial construction.
- Joe Emison, Founder & Chief Technology Officer, BuildFax
• BuildFax’s core customer base is insurance companies, which spend billions of
dollars annually on roof losses
• The company initially built predictive models based on ZIP codes and other general
data, but building the models was complex and the results did not provide enough
differentiators
• BuildFax now uses Amazon Machine Learning to provide roof-age and job-cost
estimations for insurers and builders, with property-specific values that don’t need
to rely on broad, ZIP code-level estimate
• Models that previously took six months or longer to create are now complete in four
weeks or fewer

64. Do you want to talk more about it?
Let´s schedule a meeting.

65. MUCHAS GRACIAS!

66. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Traditionally, Analytics Used to Look Like This
OLTP ERP CRM LOB
Data Warehouse
Business Intelligence Relational data
TBs-PBs scale
Schema defined prior to data load
Operational reporting and ad hoc
Large initial capex + $10K–$50K / TB / Year

67. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Data Lakes Extend the Traditional Approach
Relational and non-relational data
TBs-EBs scale
Schema defined during analysis
Diverse analytical engines to gain insights
Designed for low-cost storage and analytics
OLTP ERP CRM LOB
Data Warehouse
Business
Intelligence
Data Lake
100110000100101011100
101010111001010100001
011111011010
0011110010110010110
0100011000010
Devices Web Sensors Social
Catalog
Machine
Learning
DW
Queries
Big data
processing
Interactive Real-time

68. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Data Lakes on AWS
Unmatched durability and availability at Exabyte scale
Comprehensive security, compliance, and audit
capabilities
Object-level controls
Usage and cost analysis insight into your data
Most ways to bring data in
Twice as many partner integration
DATA LAKE
A m a z o n S 3
A m a z o n G l a c i e r
A W S G l u e
Machine Learning
Analytics
Internet of Things
Snowball
Snowmobile Kinesis
Data Firehose
Kinesis
Data Streams
Kinesis
Video Streams

69. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Data Lakes on AWS
DATA LAKE
A m a z o n S 3
A m a z o n G l a c i e r
A W S G l u e
Amazon SageMaker
AWS Deep Learni ng
AMI s
Amazon Rekogni ti on
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Transl ate
Amazon Transcri be
Amazon Pol l y
Machine Learning Analytics Internet of Things
AWS IoT Core
AWS Greengrass
AWS IoT Anal y ti cs
Amazon FreeRTOS
AWS IoT 1-Cl i ck
AWS IoT Button
AWS IoT Dev i ce Management
AWS IoT Dev i ce Defender
Amazon Athena
Amazon EMR
Amazon Redshi f t
Amazon El asti csearch Serv i ce
Amazon Ki nesi s
Amazon Qui ckSi ght