1.
TD Ameritrade’s Journey from Data
Warehouses to Data Lakes
January 31, 2017
Informatica Architecture Series

2.
Today’s Speakers
Krishna Sarma
Director, Data Development,
Data Warehouse, BI & Big Data
TD Ameritrade
David Lyle
VP Business Transformation
Services
Informatica
Amit Kara
Big Data Solutions Expert
Informatica

3.
David Lyle

4.
By 2017,
Marketing will
spend more on
technology
than IT
- Gartner

5.
But CMOs do
not want to be
CIOs

6.
Reduce Customer Churn
Better Marketing ad hoc Analysis
Better Up-Sell / Cross-Sell
Increase Revenue
Intelligence: Next best step
Understand Marketing Attribution
Who is ready to buy now?
Better Lead Conversion
Increased Wallet Share
Marketing Business Outcomes
Acquire New Customers
Increase Return on Marketing Investment
Build Customer Database

7.
Data is the #1 technical
bottleneck!
Example Problem: Analytics
86% surveyed:
“At best only somewhat
effective at meeting the
primary objective of the
data and analytics
program.”

8.
The CMO View
“Data is our
competitive
advantage!”
“Everything in
Marketing has
analytics.”
“IT is just too
slow to deliver
the data.”
“Marketing needs
data self-service
to succeed!”
“Sometimes fast
is more important
than perfect.”

9.
The CIO View
“My Data
Warehouse is rock
solid, but inflexible
and costly for new
Marketing
requirements.”
“Big Data is
interesting, but
we need to show
business value to
Marketing.”
“Need to enable
Marketing to self-
serve data.”
“Need to deliver
new data at the
pace & quality that
Marketing
requires.”
“The organization
wants cloud
analytics but data
will be even
harder to
manage.”

10.
Analytics: Data Challenges
Challenges
 Must leverage existing investment
 Marketing expects fast IT data delivery
 Data locked in application silos
 Data volume
 Data complexity – 50% external
 Lack of trust in the data
Newer Requirements
 Want to leverage new analytics technology
 Want real time data updates & decisions
 Moving to hybrid/cloud deployment
 Moving from reporting to predictive
 Business self-service for data
 Need business-lead data governance
Business Impact
Unable to deliver clean, trusted & timely
data in the timeframe required for
marketing initiatives

11.
The Data Warehouse is the Beginning of a Journey
Data Warehouse: Strengths
• Standardized data
• “Bet your career” Business
decisions
• Centralized reporting
• High reliability
• Stability
Data Warehouse: Limitations
• Slow to adapt / change
• May not handle new data types
• Not suitable for ad hoc analysis
• Not suitable for self-service
• May not handle larger volumes
/ streaming data
• Does not support transactional

12.
Everybody’s Journey Will Vary
Data Warehouse
Data Warehouse Appliance
Cloud Data Warehouse
Cloud Data Lake
On-premise Data Lake
…NOTHING goes away!

13.
"The need for increased agility and
accessibility for data analysis is the
primary driver for data lakes."
Andrew White -
13

14.
An Example Customer Journey
• ETL for DW & Applications
• Added Realtime
• Data Quality
B2B
• Cloud connectivity - SFDC
• MDM
• Big Data

15.
High Quality/Controlled Flexibility / Innovation
How many widgets did I sell
yesterday?
Questions Who should I sell to next and what
should I offer?
Structured & processed data Data Types Any or no data structure
Summarized, consolidated data Data Level Atomic data
Schema on write Processing Schema on read +++
Adding Data: 3-6 months Agility Highly fluid for additions
More mature (improving) Governance
& Security
Emergent
Data Warehouse vs. Data Lake
Data Warehouse Data Lake

16.
What Marketing Data Goes Where?
Data
Warehouse
Marketo
CRM
ERP
Log/Clickstream
Industry
Mobile / Geo
Social/Online
Sensor
Image / Video
Voice
Trusted historical data
Operationalized Insights
Marketing
Data Lake
swamp
pond
lake

17.
#IWT16
Informatica Data Lake Solution
Data
Warehouse
Marketo
CRM
ERP
Data Sources
Marketing
Data Lake
swamp
pond
lake
Informatica
Big Data Management
Data Integration Data Quality/Governance Data Security
Enterprise
Information
Catalog
Intelligent
Data Lake
Other…
Other
OnPrem
Cloud
Apps
Master
Data Mgmt.

18.
#IWT16
Informatica Marketing Technology Stack
CRM
Predictive
Marketing
Web Content
Management
SEO and ABM
Enterprise
Data
Warehouse
Marketing
Automation
Marketing
Intelligent
Data Lake
Informatica Marketing-Lake Example
Customers
and Prospects
informatica.com
Marketing
and Sales
Actionable
Insights
Analytics
Social
Leads
Web
Clean, Consistent & Integrated Data
Connect Clean Master Validate Enrich Relate Share
Informatica Platform

19.
Amit Kara

20.
Building a Data Lake
Raw Data
Assets
Applications &
Databases
Internet of Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next Best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization

21.
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization
Big Data Infrastructure
Building a Data Lake

22.
Big Data Processing
Big Data Storage
Big Data Infrastructure
Building a Data Lake – Big Data Infrastructure
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization

23.
On-premise Cloud
Hadoop NoSQL Databases
Data Warehouse
Appliances
Real-Time Near Real-Time Batch Database Pushdown
Building a Data Lake – Big Data Infrastructure
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization

24.
On-premise Cloud
Hadoop NoSQL Databases
Data Warehouse
Appliances
Real-Time Near Real-Time Batch Database Pushdown
Data Lake Management
Building a Data Lake Management Solution
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization
Big Data Analytics

25.
Foundation of a Data Lake Management Solution
On-premise Cloud
Hadoop NoSQL Databases
Data Warehouse
Appliances
Real-Time Near Real-Time Batch Database Pushdown
Metadata Intelligence
Data Lake Management
Data Visualization Advanced Analytics Predictive Analytics Machine LearningRaw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization

26.
Foundation of a Data Lake Management Solution
On-premise Cloud
Hadoop NoSQL Databases
Data Warehouse
Appliances
Real-Time Near Real-Time Batch Database Pushdown
Metadata Intelligence
Big Data Management
Data Lake Management
Data Visualization Advanced Analytics Predictive Analytics Machine LearningRaw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization

27.
Foundation of a Data Lake Management Solution
On-premise Cloud
Hadoop NoSQL Databases
Data Warehouse
Appliances
Real-Time Near Real-Time Batch Database Pushdown
Metadata Intelligence
Big Data Management
Intelligent Data Applications
Data Lake Management
Data Visualization Advanced Analytics Predictive Analytics Machine LearningRaw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization

28.
Key capabilities of Data Lake Management Solution
Data Lake Management
Big Data
Integration
Big Data
Governance and Quality
Big Data
Security
Self Service
Data Preparation
Enterprise Data Catalog Data Security Intelligence
Metadata Management
Data Index Data Discovery
Metadata Intelligence Foundation
Data Blending
Data Pipeline Abstraction
Data Integration
Transformations
Data Parsing
Publish and Subscribe
Stream Processing & Analytics
Data Ingestion
Master Data Management
Data Matching & Relationships
Data Quality
Data Profiling
Data Retention &
Lifecycle Management
Data Masking
Data Encryption
Authorization & Authentication
Big Data Storage Big Data Processing Big Data Infrastructure
Data Visualization Advanced Analytics Predictive Analytics Machine Learning
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization
Big Data
Integration
Big Data
Governance
and Quality
Big Data
Security
Metadata Intelligence
Big Data Management
Intelligent Data Applications

29.
Key capabilities of Data Lake Management Solution
Data Lake Management
Big Data
Integration
Big Data
Governance and Quality
Big Data
Security
Self Service
Data Preparation
Enterprise Data Catalog Data Security Intelligence
Metadata Management
Data Index Data Discovery
Metadata Intelligence Foundation
Data Blending
Data Pipeline Abstraction
Data Integration
Transformations
Data Parsing
Publish and Subscribe
Stream Processing & Analytics
Data Ingestion
Master Data Management
Data Matching & Relationships
Data Quality
Data Profiling
Data Retention &
Lifecycle Management
Data Masking
Data Encryption
Authorization & Authentication
Big Data Storage Big Data Processing Big Data Infrastructure
Data Visualization Advanced Analytics Predictive Analytics Machine Learning
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization
Big Data
Integration
Big Data
Governance
and Quality
Big Data
Security
Big Data Management
Intelligent Data Applications

30.
Key capabilities of Data Lake Management Solution
Data Lake Management
Big Data
Integration
Big Data
Governance and Quality
Big Data
Security
Self Service
Data Preparation
Enterprise Data Catalog Data Security Intelligence
Metadata Management
Data Index Data Discovery
Metadata Intelligence Foundation
Data Blending
Data Pipeline Abstraction
Data Integration
Transformations
Data Parsing
Publish and Subscribe
Stream Processing & Analytics
Data Ingestion
Master Data Management
Data Matching & Relationships
Data Quality
Data Profiling
Data Retention &
Lifecycle Management
Data Masking
Data Encryption
Authorization & Authentication
Big Data Storage Big Data Processing Big Data Infrastructure
Data Visualization Advanced Analytics Predictive Analytics Machine Learning
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization
Big Data
Integration
Big Data
Governance
and Quality
Big Data
Security
Intelligent Data Applications

31.
Key capabilities of Data Lake Management Solution
Data Lake Management
Big Data
Integration
Big Data
Governance and Quality
Big Data
Security
Self Service
Data Preparation
Enterprise Data Catalog Data Security Intelligence
Metadata Management
Data Index Data Discovery
Metadata Intelligence Foundation
Data Blending
Data Pipeline Abstraction
Data Integration
Transformations
Data Parsing
Publish and Subscribe
Stream Processing & Analytics
Data Ingestion
Master Data Management
Data Matching & Relationships
Data Quality
Data Profiling
Data Retention &
Lifecycle Management
Data Masking
Data Encryption
Authorization & Authentication
Big Data Storage Big Data Processing Big Data Infrastructure
Data Visualization Advanced Analytics Predictive Analytics Machine Learning
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization
Intelligent Data Applications

32.
Key capabilities of Data Lake Management Solution
Data Lake Management
Big Data
Integration
Big Data
Governance and Quality
Big Data
Security
Self Service
Data Preparation
Enterprise Data Catalog Data Security Intelligence
Metadata Management
Data Index Data Discovery
Metadata Intelligence Foundation
Data Blending
Data Pipeline Abstraction
Data Integration
Transformations
Data Parsing
Publish and Subscribe
Stream Processing & Analytics
Data Ingestion
Master Data Management
Data Matching & Relationships
Data Quality
Data Profiling
Data Retention &
Lifecycle Management
Data Masking
Data Encryption
Authorization & Authentication
Big Data Storage Big Data Processing Big Data Infrastructure
Data Visualization Advanced Analytics Predictive Analytics Machine Learning
Raw Data
Assets
Applications
& Databases
Internet of
Things
Social & Web Logs
3rd Party Data
Data
Products
e-Commerce
Next best
Recommendation
High Net-Worth
Customer
Retention
Remediation
Campaign
Management
Optimization
Marketing
Operations
Optimization

33.
Informatica’s Comprehensive Solution for Data Lakes
INGEST GOVERNPREPARE SECURE ACCESSCATALOGACQUIRE CONSUME
COMPREHENSIVE SUPPORT FOR DATA PROCESSING
Spark Blaze Tez MapReduce
Catalog SearchLineage Recommendations
METADATA INTELLIGENCE
Spark Streaming
COMPREHENSIVE SUPPORT FOR DATA INFRASTRUCTURE
Data
Preparation
Business
Glossary
Record
Linkage
Sensitivity
Visualization
Publish /
Subscribe
Batch
Processing
Stream
Processing
Data
Profiling
Data
Protection
Data
Mastering
Data
Lineage
Data
Parsing
Enterprise Data
Catalog
Big Data
Relationships
Data Security
Intelligence
Broadest
Connectivity
Reusable
Workflows
Data
Quality
Informatica Data Lake Management
Relational
Social
Files
Device data
Weblogs
Applications
Data Mining
Dashboards
Files

34.
User
Informatica
Big Data Management
& Amazon EMR
Deployment
Script
Amazon RDS
Amazon EC2
Informatica
Domain
Deploying Big Data Management on AWS
One Click Deploy on AWS

35.
Informatica BDM Process Flow using EMR
Salesforce,
Adobe Analytics
Marketo
Discover & Profile Parse & Prepare
Load to Amazon
Redshift / S3
Amazon S3
Input bucket
Amazon EMR Amazon S3
Output bucket
Amazon Redshift
1
2
3 4 5
6
Corporate Data Center
(on-prem)
Databases
Application Server

36.
36
TD Ameritrade's Journey from Data Warehouses to
Data Lakes
January 31, 2017

37.
TD Ameritrade (TDA)
37
Services offered include common and preferred
stocks, futures, ETFs, options trades, mutual
funds, fixed income, margin lending, and cash
management services
Work Culture
 Agile
 Foster Innovation
 People Matter, Client Centric, Integrity First,
Work Together & Strive To Win

38.
Operational
Master Data
Analytical
Master Data
MDM
Accts
Leads
Email
Web
Orders
Quotes
VEO
Integrated Zone Data Marts
Exploration Warehouse
BI & Analytics
External Data
(Market, Vendor)
Staging
Zone
Virtual ODS
SFDC
Others
Documents
A B C
E Archival Zone
Other
Risk User DB
Marketing User DB
Finance User DB
Data Landscape at TDA without Hadoop
Common
Staging
Area
Interactive Zone
Enterprise Data Warehouse
SDB Mart
HR Mart
Client Relationship
DM
BI / Analytics
Ad-Hoc &
Standard Reports
Data Visualization
Textual Analytics
Executive
Dashboards
Exploration &
Mining
Self-Service
User
Auth
Phone
SFDC
HR
Legacy
Etc…
Analytics
Applications
This document contains confidential information for use by TD AMERITRADE Holding Corporation and its subsidiaries. 38
Departmental
Databases
WFM
D

39.
Business Drivers for Data Lake Investment
39
What we can do today vis-à-vis what we want to do going forward
a) We know what happened yesterday
i. And we want to know what's happening Today & Now ?
 How can I model risk analytics in real time to minimize our firm’s exposure?
b) We report on less variety of data (structured)
i. And we want to tie our data sets with semi / un-structured datasets (text, emails, chats, logs, social,
etc.) as the “data” world is changing
 Who is talking what @ TDA on the Social media ?
 Who is browsing what products on TDA website? And how much time s/he is spending on our
web-page ? etc.
c) With what we have, we can do good reporting & derive some Intelligence
i. And we want to derive actionable insights along with predictive modeling, sentiment analysis,
machine learning, etc.
 What does “hot” mean when we get a tweet “I feel hot today” ?
 How would my revenues be impacted in the event of a future Hurricane “Katrina” or “Sandy” ?

40.
Data Marshalling Yard @ Hadoop at TD Ameritrade
Landing Zone
Landing area for all files
Raw dump
Data Quality checks
Profiling
Masking of Sensitive data
Non Integrated
 Any apps can consume for
further processing
One stop shop for all raw
files (structured, semi-
structured & unstructured)
A
Enterprise Data Archival
Enterprise archival
For all data types
24 x 7 x 365 access
Vast & in-expensive storage
Data can be persisted for 10-15-20 yrs.
E
Exploratory Analytics & Reporting
On all data sets (structured,
semi -structured & un-structured)
Adhoc analytics, exploration
Visualization, dash boarding, scorecarding
Reporting (Tableau, BOBJ, etc.)
B
Advanced Analytics
Text mining
Sentiment analysis
Predictive analytics and modeling
Etc.
C
Application Access
Operational reporting
Client facing applications &
engines connecting to DMY
Application tier and workloads
Various other uses depending
on platform maturity
D
40

41.
Operational
Master Data
Analytical
Master Data
MDM
Accts
Leads
Emails
Web
Orders
Logs
Chat
Integrated Zone Data Marts
Exploration Warehouse
BI & Analytics
External Data
(Market, Vendor)
Staging
Zone Virtual ODS
SFDC
Others
Documents
A B C
E Archival Zone
Other
Risk DB
Marketing User DB
Finance User DB
Data Landscape at TDA with Hadoop (Phase: Crawl)
Common
Staging
Area
Interactive Zone
Enterprise Data Warehouse
SDB Mart
HR Mart
Client Relationship
DM
BI / Analytics
Ad-Hoc &
Standard Reports
Data Visualization
Textual Analytics
Executive
Dashboards
Exploration &
Mining
Self-Service
User
Auth
Phone
SFDC
Social
Text
Etc…
Analytics
Applications
This document contains confidential information for use by TD AMERITRADE Holding Corporation and its subsidiaries. 41
Departmental
Databases
WFM
D
Data Marshalling Yard (Data Lake)
@ Hadoop
X
X

42.
Operational
Master Data
Analytical
Master Data
MDM
Integrated Zone Data Marts
Exploration Warehouse
BI & Analytics
External Data
(Market, Vendor)
Staging
Zone Virtual ODS
SFDC
Others
A B C
E Archival Zone
Other
Risk DB
Marketing User DB
Finance User DB
Data Landscape at TDA with Hadoop (Phase: Walk)
Common
Staging
Area
Interactive Zone
Enterprise Data Warehouse
SDB Mart
HR Mart
Client Relationship
DM
BI / Analytics
Ad-Hoc &
Standard Reports
Data Visualization
Textual Analytics
Executive
Dashboards
Exploration &
Mining
Self-Service
Text
Analytics
Applications
This document contains confidential information for use by TD AMERITRADE Holding Corporation and its subsidiaries. 42
Departmental
Databases
WFM
D
Data Marshalling Yard (Data Lake)
@ Hadoop
X
X
X
X
Accts
Leads
Emails
Web
Orders
Logs
Chat
Documents
User
Auth
Phone
SFDC
Social
Etc…

43.
Operational
Master Data
Analytical
Master Data
MDM
Integrated Zone Data Marts
Exploration Warehouse
BI & Analytics
External Data
(Market, Vendor)
Staging
Zone Virtual ODS
SFDC
Others
A B C
E Archival Zone
Other
Risk DB
Marketing User DB
Finance User DB
Data Landscape at TDA with Hadoop (Phase: Run)
Common
Staging
Area
Interactive Zone
Enterprise Data Warehouse
SDB Mart
HR Mart
Client Relationship
DM
BI / Analytics
Ad-Hoc &
Standard Reports
Data Visualization
Textual Analytics
Executive
Dashboards
Exploration &
Mining
Self-Service
Analytics
Applications
This document contains confidential information for use by TD AMERITRADE Holding Corporation and its subsidiaries. 43
Departmental
Databases
WFM
D
Data Marshalling Yard (Data Lake)
@ Hadoop
X
X
X
X
X
The “T”
of ETL
Accts
Leads
Emails
Web
Orders
Logs
Chat
Documents
User
Auth
Phone
SFDC
Social
Etc…
Text

44.
Operational
Master Data
Analytical
Master Data
MDM
Integrated Zone Data Marts
Exploration Warehouse
BI & Analytics
External Data
(Market, Vendor)
Staging
Zone Virtual ODH
A B C
E Archival Zone
Other
Risk DB
Marketing User DB
Finance User DB
Data Landscape at TDA with Hadoop (Phase: Glide)
Common
Staging
Area
Interactive Zone
Enterprise Data Warehouse
SDB Mart
HR Mart
Client Relationship
DM
BI / Analytics
Ad-Hoc &
Standard Reports
Data Visualization
Textual Analytics
Executive
Dashboards
Exploration &
Mining
Self-Service
Analytics
Applications
This document contains confidential information for use by TD AMERITRADE Holding Corporation and its subsidiaries. 44
Departmental
Databases
D
Data Marshalling Yard (Data Lake)
@ Hadoop
X
X
X
X
X
X
No
SQL
The “T”
of ETL
Accts
Leads
Emails
Web
Orders
Logs
Chat
Documents
User
Auth
Phone
SFDC
Social
Etc…
Text

45.
Operational
Master Data
Analytical
Master Data
MDM
Integrated Zone Data Marts
Exploration Warehouse
BI & Analytics
External Data
(Market, Vendor)
Staging
Zone Virtual ODH
A B C
E Archival Zone
Other
Risk DB
Marketing User DB
Finance User DB
Data Landscape at TDA with Hadoop (Phase: Fly)
Common
Staging
Area
Interactive Zone
Enterprise Data Warehouse
SDB Mart
HR Mart
Client Relationship
DM
BI / Analytics
Ad-Hoc &
Standard Reports
Data Visualization
Textual Analytics
Executive
Dashboards
Exploration &
Mining
Self-Service
Analytics
Applications
This document contains confidential information for use by TD AMERITRADE Holding Corporation and its subsidiaries. 45
Departmental
Databases
D
Data Marshalling Yard (Data Lake)
@ Hadoop
X
X
X
X
X
No
SQL
The “T”
of ETL
Application Access
Operational reporting
Client facing applications &
engines connecting to DMY
Application tier and workloads
Various other uses depending
on platform maturity
Accts
Leads
Emails
Web
Orders
Logs
Chat
Documents
User
Auth
Phone
SFDC
Social
Etc…
Text

46.
Hadoop at TD Ameritrade – Lessons Learning 
46
 If you are not making mistakes then you are not learning
 Evolutionary approach over Revolutionary
 Data can be useful even before it is perfected
 A goal without a plan is only a wish

47.
Hadoop at TD Ameritrade – Tips & Tricks
47
1. Network bandwidth & Firewalls
2. Organize your datasets:
a) Velocity (Batch, NRT, RT)
b) Variety (logs, email, text, chats, social, structured, etc.)
3. Data profiling
4. Data Ingestion frameworks
5. Begin with non-SII/PII datasets
6. Light Governance (to begin with)

48.
Best Practices – What our customers tell us
 Plan for Cloud and on-premise (Hybrid)
 Do Look for a data management platform that supports all use cases
 DO connect your Data Lake with a business initiative
• Start small, show value quickly
 DO leverage your current investment
• Current data management
• Data warehouse / analytics
• Data Governance
 DON’T create new silos of data / technology
 DO leverage new kinds of data, new technology- if they can accelerate
business value delivery

49.
Best Practices for Architects
 DO design your architectures to specifically enable these benefits
• Cloud for time-to-value and flexibility
• Data Lakes for flexibility and innovation
 DO plan bi-directional data flows from Data Warehouse to Data Lake
 DO leverage cloud, big data, NoSQL, Columnar… as business needs
require
 DO Standardize on a single data management platform
• High productivity & flexibility
• Pre-integrated: easy to maintain, upgrade
• Connects to any data source or target
• Supports big data, on-premise, cloud
• Handles all of your integration use cases
• Enables re-usable people, skills, code
42% prefer an
integrated
DI suite.
(#1 response)
TDWI

50.
Resources
February 2nd, 2017
Informatica Marketing Data
Lake Demo
bitly.com/infalake
March 8th, 2017
Genesis Housing: Modern Hub
Architecture to Power Digital
Transformation
Watch for posting on BrightTalk.com
Upcoming Webinars
The Complete Marketing Data Lake Management Reference Architecture
https://www.informatica.com/datalake-ref-bdm-on-aws
Reference Architecture

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Questions?

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