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Data Lakehouse Symposium | Day 1 | Part 2

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Data Lakehouse Symposium | Day 1 | Part 2

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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.

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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Data Lakehouse Symposium | Day 1 | Part 2

  1. 1. A presentation by W H Inmon LAKEHOUSE AND WAREHOUSE – A COMPARISON
  2. 2. applications ETL Data warehouse The data warehouse infrastructure
  3. 3. 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
  4. 4. Data warehouse Data warehouse – subject oriented integrated non volatile time variant collection of data for management decisions The single version of the truth
  5. 5. Data warehouse Historical data as well.
  6. 6. Data warehouse Structured, transaction based data
  7. 7. The data lakehouse
  8. 8. Machine generated data
  9. 9. A lot of data generated
  10. 10. High business value Low business value Some data has high business value, much data has little business value
  11. 11. The data is put into a data lake
  12. 12. An analytical infrastructure is placed over the data lake
  13. 13. The data lakehouse The data warehouse How are the data lakehouse and the data warehouse The same? How are they different?
  14. 14. 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
  15. 15. 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
  16. 16. The data lakehouse The data warehouse Like cousins - Related yet still different

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