The first step towards knowing your customer is to collect and extract insights and actionable information from your data. Learn how AWS enables you to cost efficiently store any amount of data and build an agile approach to data mining and visualization - helping you to make efficient business decisions and targeted offerings.

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BI & Analytics - Datalakes on AWS
Johan Brom an
M anager, Solutions Architecture
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Today's conversation
Business drivers for a Data Lake
Designing and building
Production use cases

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Data Drives Better Decision
Making

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Outcome 1 : Modernize and consolidate
• Insights to enhance business applications and create new digital services
Outcome 2 : Innovate for new revenues
• Personalization, 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

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Legacy Data Architectures Exist as Isolated Data Silos
Hadoop
Cluster
SQL
Database
Data
Warehouse
Appliance

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Enter Data Lake Architectures
Data Lake is a new and increasingly
popular architecture to store and analyze
massive volumes and heterogeneous
types of data.

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Benefits of a Data Lake
Store and analyse all of your data,
from all of your sources, in one
centralised location.
Quickly ingest data
without needing to force it into a
pre-defined schema.
Separating your
storage and compute
allows you to scale
each component as
required
A Data Lake enables ad-hoc analysis
by applying schemas
on read, not write.

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Today's conversation
Business drivers for a Data Lake
Designing and building
Production use cases

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Data
scientists
Automation /
events
Business
users
Data
analysts
Engagement
platforms
1. More personas need access to data, through appropriate tools
2. More systems need to link to data for decision and process automation
3. Users need to be able to find information, and access it securely
Expanding access requirements

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1. Data must be captured from diverse sources at speed and scale
2. Data needs to be pulled together, breaking down traditional silos
3. Benefits need to far outweigh the costs of collection and analysis
Transactions ERP Connected
devices
Social mediaWeb logs /
cookies
Exponential growth of business data

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Important Components of a Data Lake
Catalogue
& Search
Protect
& Secure
Access &
User Interface Ingest & Store

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AWS Approach to Data Lakes

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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
101010111001010100001011111011010
0011110010110010110
0100011000010
Devices Web Sensors Social
Catalog
Machine
learning
DW
queries
Big data
processing
Interactive Real-time

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S3 is key in the
Data Lake

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Building a Data Lake on AWS
Kinesis Firehose Athena
Query Service

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Designed for 11 9s
of durability
Designed for
99.99% availability
Durable Available High performance
§ Multipart upload
§ Range GET
§ Store as much as you need
§ Scale storage and compute
independently
§ No minimum usage
commitments
Scalable
§ Amazon Redshift / Spectrum
§ Amazon EMR
§ Amazon Athena
§ Amazon DynamoDB
Integrated
§ Simple REST API
§ AWS SDKs
§ Read-after-create consistency
§ Event notification
§ Lifecycle policies
Easy to use
Why Amazon S3 for the Data Lake?

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Security
§ Identity and Access
Management (IAM) policies
§ Bucket policies
§ Access Control Lists (ACLs)
§ Private VPC endpoints to
Amazon S3
§ Pre-signed S3 URLs
Encryption
§ SSL endpoints
§ Server Side Encryption
(SSE-S3)
§ S3 Server Side
Encryption with
provided keys (SSE-C,
SSE-KMS)
§ Client-side Encryption
Audit & Compliance
§ Buckets access logs
§ Lifecycle Management
Policies
§ Versioning & MFA
deletes
§ Certifications – HIPAA,
PCI, SOC 1/2/3 etc.
Implement the right cloud security controls

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Data Ingestion into S3
AWS Direct Connect
AWS SnowballISV Connectors
Amazon Kinesis
Firehose
AWS Storage
Gateway
S3 Transfer
Acceleration

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Storing is not enough. Data needs to be discoverable.
Dark data are the information
assets organizations collect,
process, and store during
regular business activities,
but generally fail to use for
other purposes (for example,
analytics, business relationships
and direct monetizing).
Gartner
CRM ERP D ata w arehouse M ainfram e
data
W eb Social Log
files
M achine
data
Sem i-
structured
Unstructured
“
”

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AWS Glue: Data Catalog
Make data discoverable
Automatically discovers data and stores schema
Catalog makes data searchable and available for ETL
Catalog contains table and job definitions
Computes statistics to make queries efficient
Com pliance
AWS Glue
Data Catalog
Discover data and
extract schema

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Data preparation accounts for ~80% of the work.
Building training sets
Cleaning and organizing data
Collecting data sets
Mining data for patterns
Refining algorithms
Other

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AWS Glue: ETL Service
Make ETL scripting and deployment easy
Automatically generates ETL code
Code is customizable with Python and Spark
Endpoints provided to edit, debug, & test code
Jobs are scheduled or event-based
Serverless

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Amazon Athena: Interactive Analysis
$ SQL
Query Instantly
Zero setup cost;
just point to
Amazon S3 and
start querying.
Pay per query
Pay only for queries run;
save 30–90% on per-
query costs through
compression.
Open
ANSI SQL interface,
JDBC/ODBC drivers, multiple
formats, compression types,
and complex joins and data
types.
Easy
Serverless: zero
infrastructure, zero
administration
Integrated with Amazon
QuickSight.
Interactive query service to analyze data in Amazon S3 using standard SQL
No infrastructure to set up or manage and no data to load
Ability to run SQL queries on data archived in Amazon Glacier (coming soon)

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Q uickSight O verview
Integrated with AWS - Redshift, RDS, Athena, S3,
IAM, Roles, CloudTrail and more
Cloud Native - Fully managed, serverless analytics at
scale
Super Fast and Easy to Use - Backed by SPICE and
a beautiful UI
Cost Effective - Starts at $9 per user per month

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Putting it all together…

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Summary of AWS Analytics, Database & AI Tools
Amazon Redshift
Enterprise Data Warehouse
Amazon EMR
Hadoop/Spark
Amazon Athena
Clusterless SQL
Amazon Glue
Clusterless ETL
Amazon Aurora
Managed Relational Database
Amazon Machine Learning
Predictive Analytics
Amazon Quicksight
Business Intelligence/Visualization
Amazon ElasticSearch Service
ElasticSearch
Amazon ElastiCache
Redis In-memory Datastore
Amazon DynamoDB
Managed NoSQL Database
Amazon Rekognition
Deep Learning-based Image Recognition
Amazon Lex
Voice or Text Chatbots

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Queries Against an Amazon S3 Data Lake

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Event-driven ETL Pipelines

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AWS Solution Builder - Data Lake on AWS
Reference Architecture deployment
via CloudFormation
Configures core services to tag,
search and catalogue datasets
Deploys a console to search and
browse available datasets
http://amzn.to/2nTVjcp

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Processing & Analytics
Real-time Batch
AI & Predictive
BI & Data Visualization
Transactional &
RDBMS
AWSLambda
ApacheStorm
onEMR
ApacheFlink
onEMR
Spark Streaming
onEMR
Elasticsearch
Service
Kinesis Analytics,
Kinesis Streams
DynamoDB
NoSQL DB Relational Database
Aurora
EMR
Hadoop, Spark,
Presto
Redshift
DataWarehouse
Athena
Query Service
AmazonLex
Speech
recognition
Amazon
Rekognition
AmazonPolly
Text tospeech
MachineLearning
Predictiveanalytics
Kinesis Streams
& Firehose

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Today's conversation
Business drivers for a Data Lake
Designing and building
Production use cases

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“For our market
surveillance systems, we
are looking at about 40%
[savings with AWS], but
the real benefits are the
business benefits: We
can do things that we
physically weren’t able to
do before, and that is
priceless.”
- Steve Randich, CIO
Case Study: Re-architecting Compliance
What FINRA needed
• Infrastructure for its market surveillance platform
• Support of analysis and storage of approximately 75
billion market events every day
Why they chose AWS
• Fulfillment of FINRA’s security requirements
• Ability to create a flexible platform using dynamic
clusters (Hadoop, Hive, and HBase), Amazon EMR,
and Amazon S3
Benefits realized
• Increased agility, speed, and cost savings
• Estimated savings of $10-20m annually by using AWS

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AWS Solution Builder - Data Lake on AWS
Reference Architecture deployment
via CloudFormation
Configures core services to tag,
search and catalogue datasets
Deploys a console to search and
browse available datasets
http://amzn.to/2nTVjcp

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Thank you!