Your business data contains critical information about customer behaviors, operational decisions, and many factors that have financial impact on your organisation. Increasingly though, this data is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence. Speaker: Craig Stires, APAC Business Development - Big Data & Analytics, Amazon Web Services

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Craig Stires
APAC Head of Analytics, Big Data, AI, Amazon Web Services
Level 200
Driving Business Outcomes
Modern Data Architectures on AWS

2. Today's Conversation
Iterative design for business outcomes
Building for scale and for speed

4. Data analysed for benefit
Available data
Should we Collect "All The Data" and See What's in it?
COST
VALUE
Investment Value of Analytics
2010 2015 2020 2025
DataVolume

5. Starting by amassing "all your data" and dumping
into a large repository for the data gurus to start
finding "insights" is like trying to win the lottery by
buying all the tickets

6. Three Big Indicators of Individual Behaviour
Purchases Movement Influence

7. A Platform to Build Business Outcomes From Data
Purchases
Movement
Influence
Ingest/
Collect
Consume/
visualise
Store
Process/
analyse
1 4
0 9
5
Revenue Lift
Market
acquisition
Customer delight
Brand advocacy
Inventory
optimisation
Supply chain
efficiency
...

8. Design an On-Demand Experimentation Sandbox
... and Only Pay for What You Use

9. Experimentation is Fast and Cost-Efficient
Design once, automatically deploy many times
AWS
CloudFormation

10. Quickly Find the Right Outcomes, and Turn off
the Rest – Win Fast, Fail Cheap

11. Today's Conversation
Iterative design for business outcomes
Building for scale and for speed

12. AWS Cloud is a Robust Technology Infrastructure Platform
Delivered on-demand, via the internet, with pay-as-you-go pricing
Over 80 services designed for security, scale, and availability

13. Modern data architectures for business insights at scale

14. Outcome 1: Modernise and Consolidate
Enhancing business applications and creating new digital
services involves the modernisation and consolidation of
existing legacy applications and operational systems.
Business goals often consist of being an agile, well-run
organisation, and to stop missing opportunities because
people are making decisions without accurate insights.#FOMO

16. Common initiatives
• Insights: 360 view of the business
• Digitisation: Web-service that gives on-demand insights
• Data monetisation: Enrich, aggregate, and sell business data
Outcome 1: Modernise and Consolidate

17. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Business users
External buyers
Data analysts
Start with the business case, and the personas

18. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Business users
External buyers
Data analysts
Extract and ingest data from on-premise systems and internet-native sources
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces
Transactions
Web logs /
cookies
ERP

19. Decouple Storage and Compute
Legacy design was large databases or data
warehouses with integrated hardware
Big Data architectures often benefit from
decoupling storage and compute

20. Amazon
S3
Highly available object storage
99.999999999% data durability
Replicated across 3 facilities
Virtually unlimited scale
Pay only for usage, no pre-provisioning
Event notifications to trigger actions

21. Amazon
EMR
Fully managed Hadoop
Optimized with S3
Autoscaling for elasticity
Transient and long running clusters
Integration with AWS Spot Market

22. Hadoop at Scale – Best When Built for Purpose
Large Scale ETL
• "finish before 5a"
• Time insensitive
• Great for leveraging
Spot
• Use EMRFS w/S3
Analytic modelling;
iterative discovery
• "massive grid processing"
• On-demand from 9a-6p
• Agile binning strategies
• Use EMRFS w/S3
Un/semi-structured
data processing
• "process stream chunks"
• Runs 24x7
• Use EC2 RIs
• Use S3/Lambda triggers

23. Fully managed
MPP SQL database - fully relational
Optimised for analytics
Gigabytes to Petabytes
Less than 1/10th the cost of traditional
Data warehouse technologies
Amazon
Redshift

24. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Business users
External buyers
Data analysts
Process data for ETL, cleansing, tagging, and place into Staged Data (Data Lake)
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETLTransactions
Web logs /
cookies
ERP

25. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Business users
External buyers
Data analysts
Secure all data and services, and enable governance
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
AWS
CloudTrail
AWS
IAM
Amazon
CloudWatch
AWS
KMS
Transactions
Web logs /
cookies
ERP

26. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Business users
External buyers
Data analysts
Load the Data Warehouse and other database platforms
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Transactions
Web logs /
cookies
ERP
AWS
IAM

27. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Business users
External buyers
Data analysts
Serve users through BI tools, dashboards, or API access
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Amazon
QuickSight
Amazon
API Gateway
Transactions
Web logs /
cookies
ERP
AWS
IAM

28. Outcome 2: Innovate for New Revenues
Organisations start operating based on what they know
about their customers, and can approach new ventures in
terms of confidence levels.
Product launches, campaigns, supply chain management,
packaged services, and customised offerings are designed
and executed based on predictive models.#KnownUnknown

30. Common initiatives
• Personalisation: Refine market approaches on optimal segments
• Predict demand: Guide business owners to select best scenarios
• Risk measurement: Create freedom to act by quantifying exposures
Outcome 2: Innovate for New Revenues

31. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital servicesStart with the business case, and the personas
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Transactions
Web logs /
cookies
ERP
AWS
IAM

32. Real-Time data processing large distributed streams
Elastic capacity scales to millions of events / second
Handle incoming stream events in real-time
Stream storage replicated across 3 facilities
Amazon
Kinesis

33. Interactive query service to analyse data in Amazon
S3 directly using standard SQL
No need to move data
No infrastructure to setup & manage
Fast – results within seconds
Pay for only the queries you run
Amazon
Athena

34. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital servicesData analysts can process data as "schema on read"
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
AWS
IAM

35. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital servicesData scientists produce predictive models and other analysis
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
Amazon EMR
MLlib
Deep Learning
Amazon ML
AWS
IAM

36. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital servicesEngagement is automated, based on advanced analytics
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
Advanced
Analytics
MLlib
AWS
IAM

37. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
Advanced
Analytics
MLlib
AWS
IAM

38. Modern data architectures for real-time analytics and engagement

39. Outcome 3: Real-Time Engagement
Provide superior customer service by responding to
opportunities in real time. Fulfil requests for products
or services in an automated fashion to create a strong
competitive advantage over those that are unable to.
Adding another layer of opportunity and complexity is the
use of vast streams of data from devices that are
measuring location, video, behaviours, environmental
conditions, and more.
#WindowOfOpportunity

41. Common initiatives
• Interactive CX: Natural customer journeys with adaptive interfaces
• Event-driven automation: Triggered execution of business process
• Fraud detection: Protect customer and business interests
Outcome 3: Real-Time Engagement

42. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
Advanced
Analytics
MLlib
Event Capture
Amazon Kinesis
Events are captured in the speed layer
Stream Analysis
Amazon EMR
Automation / events
AWS
IAM

43. Fully managed serverless compute
Can load data sources (S3, DynamoDB)
automatically into your data architecture
(e.g. Amazon Redshift)
Can be triggered in real-time by incoming events
in Amazon Kinesis, or changes to Amazon
S3 buckets
Amazon
Lambda

44. Amazon Rekognition
Image Recognitions and Analysis
powered by Deep Learning which
allows to search, verify and organise
millions of images
Easy to use Batch Analysis Real-time
Analysis
Continually
Improving
Low Cost

45. Maple
Villa
Plant
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard

46. Demographic Data
Facial Landmarks
Sentiment Expressed
Image Quality
Brightness: 25.84
Sharpness: 160
General Attributes

47. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
AWS
IAM
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
Advanced
Analytics
MLlib
Event Capture
Amazon Kinesis
The event handler sends for scoring, getting in-flight enrichment signals
Stream Analysis
Amazon EMR Event Scoring
Amazon AI
Event Handler
AWS Lambda
Automation / events

48. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
AWS
IAM
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
Advanced
Analytics
MLlib
Event Capture
Amazon Kinesis
Published models are used, or black box services are called
Stream Analysis
Amazon EMR Event Scoring
Amazon AI
Event Handler
AWS Lambda
Automation / events

49. Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Modernise and Consolidate
Insights to enhance business applications, new digital services
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces Amazon S3
Raw Data
Amazon S3
Staged Data
(Data Lake)
Amazon EMR
ETL
Amazon RedShift
Data Warehouse
Amazon RDS
Legacy Apps
AWS
CloudTrail
AWS
IAM
Amazon
CloudWatch
AWS
KMS
Data analysts
Data scientists
Business users
Engagement platforms
Amazon
ElasticSearch
Amazon Athena
Amazon
Kinesis
Connected
devices
Social media
Advanced
Analytics
MLlib
Event Capture
Amazon Kinesis
Responses are pushed for near real-time action
Stream Analysis
Amazon EMR Event Scoring
Amazon AI
Event Handler
AWS Lambda Response Handler
AWS Lambda
Near-Zero Latency
Amazon DynamoDB
Automation / events

50. Outcome 1: Modernise and consolidate
• Insights to enhance business applications and create new digital services
Outcome 2: Innovate for new revenues
• Personalisation, demand forecasting, risk analysis
Outcome 3: Real-time engagement
• Interactive customer experience, event-driven automation, fraud detection
Outcome 4: Automate for expansive reach
• Automation of business processes and physical infrastructure
Business Outcomes on a Modern Data Architecture

51. Taking your ideas and building the next big thing

52. Outcomes From a Modern Data Architecture
Start with the business case
Experiment and iterate Deploy with automation
De-couple to scale

53. Thank you!