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Data Lakehouse Symposium | Day 1 | Part 1

The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse. Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today. Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow. This is an educational event. Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.

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A presentation by
W H Inmon
DATA LAKEHOUSE –
THE BASIC ELEMENTS
All data in the corporation
Structured
data
Textual
data
Analog/IoT
data
Structured
data
Textual
data
Analog/IoT
data
Each of the different types of data have their
own unique characteristics
Structured
data Usually transaction based
record
key attribute
index
Bank transactions
Point of sale
Telephone call
Payments made
Payments received
…………………….
Structured
data
The same record type is repeated
Each record has different contents
Textual
data
Medical records
Contracts
Internet
Call centers
Warranty claims
Insurance claims
Email
………………..
Text is found everywhere
English
Spanish
Portuguese
French
Mandarin
Korean
German
Formal language
Slang
Acronyms
……………..
Voice
Written
Internet
Video
………………..
Textual
data
Textual
ETL
taxonomies
Text is transformed
Into a structured
format
Analog/IoT
Machine generated
Drones
Electric eye
Temperature gauge
Speed
Mechanical
Telemetry
…………………….
Analog/IoT
telemetry
Date Sept 2, 2021
Time 11:21 am
Location from Denver
Location to Co Spgs
Elevation
786
792
812
854
901
978
1012
1256
1469
1672
2018
2259
2871
……..
Speed
0
35
79
124
197
276
367
416
521
702
835
915
…..
Telemetry data is generated as the
rocket is launched and is measured
throughout the flight
Analog/IoT
The data lake is created by throwing data
all the data into the lake
Textual
data
Structured
data
Analog/IoT
Soon the data lake
turned into a swamp
Analog/IoT
The data swamp was not good for anyone….
Analog/IoT
The data lake needs to be turned
into a lakehouse
Analog/IoT
All this education and 95% of my job
is being a data garbageman
Data scientist
Analog/IoT
Data scientist
Ah, that’s more
like it
infrastructure
Analog/IoT
Machine generated
Time – 0912
Time – 0916
Time – 1002
Time – 1008
Time – 1017
…………….
Basic, raw measurements
High probability
High performance
Low probability
Bulk storage
Analog/IoT data is often
segmented
High probability
High performance
Low probability
Bulk storage
Date of launch
Ultimate speed
Ultimate height
Final landing point
Second by second
measurements
Structured
data
Textual
data
Analog/IoT
data
Relative volumes of data in each sector
Structured
data
Textual
data
Analog/IoT
data
Business value and the volumes of data
Structured
data
Textual
data
Analog/IoT
data
Relational format Raw data format
From a format standpoint, the structured and the textual
environments are very different from the analog/IoT
environment
Format compatibility
Structured
data
Textual
data
Analog/IoT
data
Key compatibility – very unintegrated
Content compatibility
Structured
data
Textual
data
Analog/IoT
data
In order to do analytics, there must be
some common data on which to do a
comparison
Without common data it is very difficult
to do a meaningful comparison
Structured
data
Textual
data
Analog/IoT
data
The problem is that there may be no obvious,
easy way to isolate common identifiers
Structured
data
Textual
data
Analog/IoT
data
Fortunately there are such things as
universal common connectors
Structured
data
Textual
data
Analog/IoT
data
Universal common connectors exist regardless of the
way that data has been collected
Structured
data
Textual
data
Analog/IoT
data
Universal common connector for anything
geography
time
dollar amount
General common connectors
Structured
data
Textual
data
Analog/IoT
data
Universal common connector for humans
gender
age
race
Common connectors for humans
Structured
data
Textual
data
Analog/IoT
data
Universal common connector for physical objects
weight
color
cost
size
shape
Common connectors for objects
SOME EXAMPLES
Universal common connector
Healthcare – outcomes analysis
Did the medicine work?
Did the vaccination work?
Did the operation have the right effect?
Outcome analysis
Structured
data
Textual
data
Analog/IoT
data
Prolia
Estrogen
Vitamin D
Algaecal
Calcitonin
Sales of -
Doctor’s notes
tests
diagnosis
procedure
medication
history
……………
X rays
date
location
patient age
examination results
Structured
data
Textual
data
Analog/IoT
data
What medicines
have been
purchased
What medicines
have been
prescribed and/or
discussed with
doctors
By state
By age
By gender
By state
By age
By gender What outcomes have
been achieved
By state
By age
By gender
What medicines
have been
purchased
By state
By age
By gender
What medicines
have been
prescribed and/or
discussed with
doctors
By state
By age
By gender
What outcomes have
been achieved
By state
By age
By gender
Analyses –
how does treatment in Utah vary from treatment in Oregon?
is Prolia more effective than estrogen?
when patients are treated with Algaecal, what other side effects are noticed?
do women have better results than men?
how much does age affect –
the types of treatment for osteoporosis
the effectiveness of treatment
whether men react differently than women
What medicines
have been
purchased
By state
By age
By gender
What medicines
have been
prescribed and/or
discussed with
doctors
By state
By age
By gender
What outcomes have
been achieved
By state
By age
By gender
When you have both treatment and outcome data together, you can
answer – for the first time – important questions about treatment,
medication, dosage, side, effects, demographics of treatment
You can match outcome with treatment
What medicines
have been
purchased
By state
By age
By gender
What medicines
have been
prescribed and/or
discussed with
doctors
By state
By age
By gender
What outcomes have
been achieved
By state
By age
By gender
The result is healthier people
and longer life and better quality
of life
Manufacturing
Structured
data
Textual
data
Analog/IoT
data
Sales data
unit sold
date of sale
location of sale
customer address
Warranty claims
unit
unit type
defect
severity
in use desc
Manufacturing data
unit id
lot id
date of manufacture
machine used
operator
Textual
data
Structured
data
Analog/IoT
data
Units sold
Date of sale
Location of sale
Unit id
Defect description
Date of warranty
Unit id
Machine used for manufacture
Date of manufacture
Operator
Lot id
Manufacture telemetry
Unit id
Unit id
Unit id
Units sold
Date of sale
Location of sale
Unit id
Defect description
Date of warranty
Unit id
Machine used for manufacture
Date of manufacture
Operator
Lot id
Manufacture telemetry
Analyses –
what manufacturing machines are producing defects
what manufacturing machines are not producing defects
what operators are producing defects
what operators are not producing defects
what telemetry needs to be adjusted
under what conditions are defects created
…………………………………………………………
Units sold
Date of sale
Location of sale
Unit id
Defect description
Date of warranty
Unit id
Machine used for manufacture
Date of manufacture
Operator
Lot id
Manufacture telemetry
With all of this data together and able to be analyzed
you can now tell what defects can be corrected and what
conditions cause defects to occur. The manufacturing
process can be materially improved
Units sold
Date of sale
Location of sale
Unit id
Defect description
Date of warranty
Unit id
Machine used for manufacture
Date of manufacture
Operator
Lot id
Manufacture telemetry
Now manufacturing can be done
efficiently and in a cost effective
manner
With analytics from the data lakehouse, you can improve the
lives and livelihood of many people

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