The document outlines the journey of big data from various sources to data science, highlighting the importance of data curation, storage solutions like data lakes and data warehouses, and the use of business intelligence for performance monitoring. It emphasizes the necessity for scalable storage and the ability to perform self-service analysis through data marts. The author invites feedback and comments on the presented concepts.

1. Data Juice
The Essence of Big Data: from Data Sources to Data Science.
A Data journey through Innovation, Information and Knowledge
Davide Mauri – info@davidemauri.it
Last Update: April 2017, v 4.0

2. Data Sources
Huge amounts of Data.
Freshly Produced.
Consumed one piece at time.

3. Big Data
Volume, Variety, Velocity: Data
will come, no matter if you’re
prepared or not

4. Data Lake
All harvested data, from any
source. Good or bad, store it.
Cheap, scalable storage is
mandatory.

5. Data Curation (or ETL)
Clean, Select, Process,
Integrate Data

6. Data Warehouse
Data Warehouse
Keep processed useful data in
a safe place, easily accessible
for delivery and further
processing

7. Data Mart
Ready-to-be-consumed Data,
with specific additional
processing

8. Data Mart
Data Mart
More than one way to
consume stored data is
possible.

9. Self-Service BI
Self-Service Analysis
Don’t like the Data Mart?
DIY. Data Warehouse is here
to serve.

10. Data Science
Business Intelligence
Use gathered data to drive
and monitor business and its
performances

11. Data Science
Data Science
New way of using data, never
thought before. Even would-
be-discarded big data.

12. Data Juice
Feedback and Comments more than welcome.
Davide Mauri – info@davidemauri.it