The document discusses the challenges and definitions of big data, highlighting the inadequacy of Gima students in handling big data in their careers. Key examples include the large amounts of data generated by the Large Hadron Collider and the SKA telescope, emphasizing its importance in various fields like finance and healthcare. The text contrasts the roles of data engineers and data scientists, suggesting that both need specific skills to navigate the big data landscape.

1. Big Data, Big Headache?

2. Statement
“GIMA students are not well equipped to
deal with Big Data in their future careers”

3. Who am I?
Tom Broersen

4. Who am I?
Tom Broersen

5. Who am I?
Tom Broersen

6. Big Data, Big Headache?

7. Ask the co-workers

8. Ask Google

9. MANY different definitions
“Too big for one computer”
“When the size becomes a problem”
“Distributed systems and non-
conventional database technologies”

10. MANY different definitions
“Too big for one computer”
“When the size becomes a problem”
“Distributed systems and non-
conventional database technologies”

11. All is relative… AHN3 and NDW
AHN3: 1.5 billion points
> 1TB in size
NDW traffic data: 27.000
measurements per minute

12. All is relative… LHC @CERN
The Large Hadron Collider (LHC) at CERN Data Centre:
• 600 million collisions per second
• Filtered to 1GB/s (roughly 80.000 GB/day)
• >> 10.000 processing cores

13. All is relative… SKA TELESCOPE
The SKA project (2024):
• One exabyte of data every day
(1000 * 1000 * 1000 GB)
• Requires millions of
traditional servers

14. All is relative…
AHN3: 1TB
NDW:
27.000 per
minute
LHC:
80TB/day
SKA:
exabyte/day

15. Who cares?
“The definition of Big Data? Who
cares? It’s what you’re doing with it.”

16. It’s not about the definition
BIG DATA
< What we know…
< The rest…

17. How will Big Data change our lives?

18. How will Big Data change our lives?
Comprehensive picture of the
patient as an individual

19. How will Big Data change our lives?
De-centralised power grid

20. How will Big Data change our lives?
Finance

21. How will Big Data change our lives?
Shift planning and avoiding “out
of stock conditions”

22. High potential in GEO-data

23. Geo (Big) Data apps

24. But: minor role of GIS

25. Big Data, Big Headache?

26. But: minor role of GIS

27. Are you well equipped?
Data Engineer
“Hacker mindset”
“Builds the product”
System implementation
Scripting (Python, JavaScript)
Databases (SQL, NoSQL)
Big Data (Hadoop, Spark)
Data Scientist
“Curious about data”
“Asks the right questions”
Statistics (R)
Story-telling
Visualisation and cartography
Analysis (ESRI ArcGIS)

28. Are you well equipped?
Data Scientist
“Curious about data”
“Asks the right questions”
Statistics (R)
Story-telling
Visualisation and cartography
Analysis (ESRI ArcGIS)

29. Are you well equipped?
Data Engineer
“Hacker mindset”
“Builds the product”
System implementation
Scripting (Python, JavaScript)
Databases (SQL, NoSQL)
Big Data (Hadoop, Spark)

30. Are you well equipped?
Data Scientist
“Curious about data”
“Asks the right questions”
Statistics (R)
Story-telling
Visualisation and cartography
Analysis (ESRI ArcGIS)

31. Are you well equipped?
Data Engineer
“Hacker mindset”
“Builds the product”
System implementation
Scripting (Python, JavaScript)
Databases (SQL, NoSQL)
Big Data (Hadoop, Spark)

32. Are you well equipped?
Data Engineer
“Hacker mindset”
“Builds the product”
System implementation
Scripting (Python, JavaScript)
Databases (SQL, NoSQL)
Big Data (Hadoop, Spark)
Data Scientist
“Curious about data”
“Asks the right questions”
Statistics (R)
Story-telling
Visualisation and cartography
Analysis (ESRI ArcGIS)

33. Statement
“GIMA students are not well equipped to
deal with Big Data in their future careers”

34. Are you well equipped?
Data Scientist Data Engineer
“Curious about data”
“Asks the right questions”
“Hacker mindset”
“Builds the product”
System implementation
Scripting (Python, JavaScript)
Databases (SQL, NoSQL)
Big Data (Hadoop, Spark)Statistics (R)
Story-telling
Visualisation and cartography
Analysis (ESRI ArcGIS)

35. Are you well equipped?
Data Scientist Data Engineer
“Curious about data”
“Asks the right questions”
“Hacker mindset”
“Builds the product”
System implementation
Scripting (Python, JavaScript)
Databases (SQL, NoSQL)
Big Data (Hadoop, Spark)Statistics (R)
Story-telling
Visualisation and cartography
Analysis (ESRI ArcGIS)

36. How to build a great resume?
(Modern)

37. Who has the future?

38. Questions
Tom Broersen
tbroersen@tensing.com