1.
Name · Title · Dunn Solutions
2017
Jose Hernandez· Director of Analytics · Dunn Solutions

2.
Today’s Agenda
Introduction to Dunn Solutions Group
What is a Data Lake?
You Need a Data Lake
Q&A

3.
Dunn Solutions Delivers Velocity to Businesses
Dunn Solutions is a digital commerce
and business transformation consultancy
focused on delivering velocity to our
clients. Velocity is achieved by the
combination of both speed and
direction.
Dunn Solutions helps our clients achieve
speed by automating business
processes and direction using advanced
analytics. Our teams align with
organizations to optimize their unique
processes and help them discover the
most profitable routes to business
success.

4.
Dunn Solutions is a full-service IT consulting firm
founded in 1988
Raleigh, NC
Delivery  Training
Bangalore, India
Delivery
Minneapolis
Delivery  Training
Chicago
Delivery

5.
Practice Areas
Application
Development
• Portals
• e-Commerce &
Content Managed
Websites
• Mobile App
Development
• Custom App
Development
• Search Engine
Optimization
Training
• Certified SAP,
Liferay, Microsoft
• Classroom, On-
site, Computer
Based & Virtual
• Mentoring &
Custom Training
Frameworks
• Accountable Care
Orgs (ACO’s)
• Corporate Legal
• Higher Education
• Optical Shop
Solutions
Analytics
• Data Lakes
• IoT
• Predictive Analytics
• Machine Learning
• e-Commerce
• Analytics
• Cloud - BI
Platforms
• DW & Data
Integration

6.
Selected Clients

7.
PartnershipsPartnerships

8.
Business Intelligence
Big Data
Data IntegrationBusiness Analytics
Data Repositories
• KPI’s and Metrics
• Dashboards
• Exploration and
Visualization
• Ad Hoc Analysis &
Reporting
• Data Mining
• Predictive Analytics
• Prescriptive Analytics
• R, AzureML
• Hadoop,
• Hive, Sqoop, Spark
• NoSQL
• MapReduce
• Data Lakes
• Columnar
• In-memory
• EIM (Data Integration &
Data Quality
• Dimensional Modeling
Analytics Practice

9.
• Develop Forecasting Models
• Productionizing Predictive Models
• Retail Analytics
• Machine Learning
• Data Lakes
• Big Data
• Integration with Data
Warehouses
• Migrate your Data Warehouse to
the Cloud with Azure and AWS
• Migrate SAP BusinessObjects
deployments
• Full Lifecycle Data Warehouse
Development
• Extend Data Warehouse to the Cloud
• Massive Data Warehouses in the
Cloud
• Snowflake
Analytics Services
Migration Services
Big Data Services
Data Warehousing Services
Analytics Services in the Cloud

10.
Azure
HDInsight
Azure Event Hubs
Azure SQL Data
Warehouse
Azure Stream
Analytics
Azure Machine
Learning
Azure Data Lake
Azure Training
Partner
Microsoft Azure Consulting Services

11.
Amazon
EMR
Amazon
Machine Learning
Amazon Kinesis
Firehose
Amazon Lambda
Amazon
DynamoDB
Amazon Redshift
Amazon
IoT
Amazon Web Services Consulting

12.
• U.S. based management of teams
and client communications
• All resources interviewed and
approved by DSG leadership
• Right Model/Right Project
• U.S. only
• U.S.-- India
• India only (EMEA clients)
• Mature and proven
• Phased approach
• Project sensitive Software
• Engineering methodology
• Certified Quality Processes
• Current technology awareness
• Risk awareness
Process
Technology
People
Dunn Solutions Global Delivery Model

13.
Today’s Agenda
Introduction to Dunn Solutions Group
What is a Data Lake?
You Need a Data Lake
Q&A

14.
Jose Hernandez, Director of Analytics

15.
Warning!
Today’s data consumer is very demanding, and rightly so!
80% of consumers need KPIs and operational
data – The data warehouse is ideal for them.
10-15% of consumers do more analysis; they use the
data warehouse as a source, but dive back into
source systems to get more data.
The rest of the consumers do very deep data analysis – this
includes data scientists. They are voracious data consumers
and data creators! (IT can’t keep up with them)

16.
Access to information…
• What information?
• When?
• How much?
Analyze the data..
• What tools?
• What kind of data?
Savvy Data Consumers Needs
A: any, all, even data not though of
A: anytime, now would be great
A: all of it, as much as there is
A: whatever tool is need (lots of
great tools are available
commercially and open source)
A: all kinds

17.
• Business users rely heavily on IT
• IT controls access to the data
• Accessing data across sources is very challenging
• Schema on write*
What about the enterprise data warehouse?
• Does not provide just-in-time data
• Requires lots of lead time
• Limited to the “required” data
Traditional Data Storage and Management Challenges
KPI
The demand for data has never been greater!
*Sorry  I could not avoid this terminology, more in a bit….

18.
The Data Lake Provides Relief

19.
A Data Lake is a storage repository that holds a vast amount of raw data in its native
format, including structured, semi-structured, and unstructured data. It also provides
compute power to work the data.
What is a Data Lake?
The Data Lake is about democratization of
information
It provides your organization a cost effective way
to store information for later processing
It lets your information consumers and researchers
focus on finding the next big thing, not wasting
time finding the data
For the techies in the crowd….

20.
James Dixon of Pentaho is credited with coining
the phrase “Data Lake”
Origin of the Data Lake
Dixon’s analogy…
Think of the data mart as
bottled water; cleansed,
packaged and delivered for
your consumption
The data lake is a man made
reservoir of water in its natural
state, no processing

21.
Feed the data starved users
Make it easy to consume and combine
Deliver the data just-in-time
Store all kinds of data (whether you have a specific
need today or not), and lots of it
Worry about how it’s going to be used later
(schema on read)
Provide boundless playgrounds
• To store data
• To process data
Purpose of the Data Lake

22.
Warning!
The Data Lake does not replace the Enterprise Data Warehouse!

23.
Comparing the Data Lake to the Data Warehouse
Data Warehouse
• Data focuses on Business
Processes
• Highly processed &
massaged
• Tabular & structured
• Lots of effort on design &
build
• Optimized for data retrieval
• Highly governed
Data Lake
• Stores everything
• Unprocessed / RAW
• Unstructured, semi-
structured, structured
• Democratization of data
• Shared data stewardship
• Provides compute power

24.
It’s Not Just About Data Storage
Storing and accessing data is only part of the Data Lake’s Purpose
The Data Lake must also provide the ability to:
• Massively process data (usually in place)
• Process and combine structured, semi-structured and un-
structured data
• Grow and shrink in both storage and compute power as
needed
• Onboard data very fast
• Perform advanced analytics (massively process data)

25.
Supporting Top-Down and Bottom-Up
Data Warehouses use
the Top-Down approach
Data Lakes use the
Bottom-up approach
From specific instance into
a generalized conclusion
From generalized principles
(known to be true) to a
specific conclusion
Descriptive Predictive

26.
What Does a Data Lake Look Like?

27.
Filling the Data Lake
Types of data
• Structured Data
• Semi-structured Data
• Unstructured Data
No schema is applied at
load time
Data loads very fast
The Data Lake is infinitely
deep and can hold all
data

28.
Supports many uses
• Data Exploration
• Staging for the Data
Warehouse
• Data enrichment
• Predictive analytics
• Mixing disparate data
• Apply schema on
demand (on read)
• Processing massive
amounts of data
• Sandboxes for
experimentation
Consuming from the Data Lake

29.
Warning
Don’t let your Data Lake turn into a data swamp!
It’s not the Wild, Wild,
West. Governance is
still needed.
Data consumers must
also be citizen data
stewards.
Include metadata (data
about your data)
Don’t contaminate the
Data Lake with bad data
(get it from trusted
sources)
Data Lakes hold all
data; however set and
enforce boundaries.
Have a vision for your
data lake; know what it
will be used for.

30.
Access and Security
• It’s a data playground,
even playgrounds have
rules
• Not all the data should
be available to all users
(confidential information
that must be protected)
• Is the data sensitive in
nature? Are there laws
governing the data that
require encryption?
Data Quality
• poor quality data, don’t
put it in your data lake
• Trust the source
Security and Governance

31.
Today’s Agenda
Introduction to Dunn Solutions Group
What is a Data Lake?
You Need a Data Lake
Q&A

32.
Voracious Data Consumers Must Be Served!
Getting back to the 10% of users that need
all the data; the Data Scientists
Your organizations success
and survival depends on
• Innovation
• Efficiency
• finding the next big thing
• getting (and keeping) an
edge
The data scientists and data analysts
give you the ability to do this. The
data lake supports:
• Predictive Analytics
• Prescriptive Analytics
• Machine Learning
• Experimentation (A/B Testing)
• Qualitative data analysis – help
steer strategic decisions

33.
How Does a Data Lake Complement the EDW?
Your enterprise data warehouse is home to historical data and metrics that
feed your KPIs, PIs based on your business processes.
It does this by extracting, transforming and loading the data required to
support your “known” KPIs and metrics.
What if you determined that some data element was needed to provide a KPI
you should have been tracking?
You would add that to your data warehouse and start populating from that
point forward.
Too bad, wish I would have thought of this sooner, there are some historical
trends that I would be able to identify 

34.
Give Super Powers to your Data Warehouse!
Imaging you could go back in time!
In the previous scenario you did not have the historical data because:
a. It was not being captured because it wasn’t considered
b. The EDW staging area is transient and typically only goes back for a short
period of time
The Data Lake would have given your data warehouse the ability to go back in
time!
The data lake can serve as a great staging area for your EDW. It can store
transactional data from the beginning of time:
a. Letting you go back in time and reconstruct your EDW to incorporate the
information you did not consider
b. Also it would allow you to rebuild your EDW from day one in the event of a
catastrophic failure

35.
Warning!
Deploying a data lake is very expensive
and challenging. So don’t!

36.
Do It in the Cloud!
Reliable &
Trusted
Pay for what
you use
Supporting
Tools
Easily Scales

37.
Delight Your Data Consumers!
You’re wondering whether the Data Lake can help you with your data
starved consumers.
The simple answer is yes.
You don’t have to start huge (that’s the beauty of cloud based data lakes).
We can get you started immediately.
Your data consumers will be very happy.
Contact us
info@dunnsolutions.com

38.
Question & Answers
Jose Hernandez· Director of Analytics · Dunn Solutions
Watch for more webinars featuring how Data Scientist
“do their thing” with Data Lakes in the cloud!