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A presentation by
W H Inmon
LAKEHOUSE AND WAREHOUSE –
A COMPARISON
applications
ETL Data
warehouse
The data warehouse infrastructure
applications
Data
warehouse
Gender – m/f
Distance - inches
Gender – male/female
Distance - feet
Gender – 1/0
Distance - cms Gender – m/f
Distance - feet
A fundamental transformation of data occurs
Data
warehouse
Data warehouse –
subject oriented
integrated
non volatile
time variant
collection of data for management decisions
The single version of the truth
Data
warehouse
Historical data as well.
Data
warehouse
Structured, transaction based data
The data lakehouse
Machine generated data
A lot of data generated
High business value
Low business value
Some data has high business value,
much data has little business value
The data is put into a data lake
An analytical infrastructure is placed over the data
lake
The data lakehouse The data warehouse
How are the data lakehouse and the data warehouse
The same? How are they different?
The data lakehouse The data warehouse
The same -
Both exist to serve analytical processing
Both have vetted data – accurate, reliable
Both can look at data over a long period of time
Both have tame variant data
Both have data that once recorded cannot be changed
The data lakehouse The data warehouse
Different -
There is MUCH MORE data in the lakehouse
The data is fundamentally different –
warehouse data is transaction based data
lakehouse data is machine generated data
The data structures are very different
Relating the two types of data is difficult to do
The data lakehouse The data warehouse
Like cousins -
Related yet still different

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