The data lake style of a data warehouse architecture is a flexible alternative to a traditional data warehouse. It allows for unstructured data. When a warehousing approach requires that the data be in a structured format, there are constraints on the analyses that can be performed because not all of the data can be structured early. The data lake concept is very similar to our Late-Binding approach in that data lakes are our source marts. We increase the efficiency and effectiveness of these through: 1. Metadata, 2. Source Mart Designer, and 3. Subject Area Mart Designer.

1. Data Lake vs. Data Warehouse:
Which is Right for Healthcare?
―Jarod Crapo

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Introduction to the Data Lake
In 2010, James Dixon
introduced the concept of the
Data Lake, and his idea has
gained traction ever since.
Dixon’s Data Lake is a style of
data warehouse architecture,
which he describes as follows:
If you think of a datamart as a store of bottled water – cleansed and
packaged and structured for easy consumption – the data lake is a large
body of water in a more natural state. The contents of the data lake
stream in from a source to fill the lake, and various users of the lake can
come to examine, dive in, or take samples.”

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Introduction to the Data Lake
Dixon conceived of this
architecture as a flexible
alternative to traditional data
warehouses that keep data in
a very structured format.
Structure puts constraints on
the analyses that can be
performed and may not
accommodate future needs.
The Data Lake concept allows
for unstructured data—and
more flexibility to answer new
questions.

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The Late-binding EDW and the Data Lake
At Health Catalyst, we see a lot
of value in this idea of the Data
Lake. In fact, Dixon’s Data Lake
concept is very similar to our
Late-Binding™ enterprise data
warehouse (EDW) architecture.
What Dixon calls a Data Lake,
we call a source mart.
We bring data from our source
systems into these source marts
keeping data as raw as possible,
relying on the natural data
models of the source systems.

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Benefits of a Flexible, Late-binding EDW
Here are three key benefits that
make this the best architecture
for healthcare EDWs:
1. A user only has to go to the source
systems one time.
2. A flexible architecture enables
analysts to respond to any and all
future needs.
3. Users can scale the size of an EDW
easily with this architecture using
traditional Microsoft database tools.

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How to Drink the Data Lake’s Water
In a discussion of a Data Lake (or any
kind of data warehouse architecture),
the key question is this:
How are users going to use the water?
Health Catalyst offers three applica-
tions to increase the efficiency and
effectiveness of the Data Lake:
1. Metadata
2. Source Mart Designer
3. Subject Area Mart Designer

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How to Drink the Data Lake’s Water
Healthcare systems leaders can
use the data warehouse (lake)
to improve clinical quality or to
drive operational efficiency.
It can be used to manage an
accountable care organization.
Find an analytics partner with
proven experience driving
improvement and a flexible
EDW architecture, and slake the
thirst of physicians.

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Data Lake in Health Catalyst Social Media

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More about this topic
Early- or Late-binding Approaches to Healthcare Data Warehousing: Which Is Better
for You? Mike Doyle, Vice President
Data Warehouse Tools: Faster Time-to-Value
Doug Adamson, Chief Technology Officer
Six Reasons Why Healthcare Data Warehouses Fail
Steve Barlow, Co-founder and Senior Vice President Client Operations
I Already Have a Data Warehouse. What Applications Can I Use?
A Guide to Using Health Catalyst Tools Within Your Existing Data Warehouse
Environment
Data Warehouse Trends: What to Know Before Setting Up Your Data Warehouse
Dale Sanders, Senior Vice President of Strategy
Link to original article for a more in-depth discussion.
Data Lake vs. Data Warehouse: Which is Right for Healthcare?

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For more information:

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Jared Crapo joined Health Catalyst in February 2013 as a Vice President. Prior to coming to
Catalyst, he worked for Medicity as the Chief of Staff to the CEO. During his tenure at
Medicity, he was also the Director of Product Management and the Director of Product
Strategy. Jared co-founded Allviant, a spin-out of Medicity, that created consumer health
management tools. In his early career, he developed physician accounting systems and health claims
payment systems.