The document discusses the application of Python in business intelligence, focusing on data extraction, transformation, and loading (ETL) processes. It highlights how Python can enhance traditional data warehousing tasks, while also addressing community perceptions of its capabilities in handling scientific and financial data. The conclusion emphasizes the importance of maintaining a balance between people, technology, and processes in business intelligence.

1. Python for Business
Intelligence
Štefan Urbánek ■ @Stiivi ■ stefan.urbanek@continuum.io ■ PyData NYC, October 2012

2. python business intelligence
)

3. Results
Q/A and articles with Java
solution references
(not listed here)

5. Why?

6. Overview
■ Traditional Data Warehouse
■ Python and Data
■ Is Python Capable?
■ Conclusion

7. Business
Intelligence

8. people
technology processes

9. Data Analysis and
Extraction, Transformation, Loading
Sources Presentation
Data Governance
Technologies and Utilities

10. Traditional Data
Warehouse

11. ■ Extracting data from the original sources
■ Quality assuring and cleaning data
■ Conforming the labels and measures
in the data to achieve consistency across the original sources
■ Delivering data in a physical format that can be used by
query tools, report writers, and dashboards.
Source: Ralph Kimball – The Data Warehouse ETL Toolkit

12. Source Staging Area Operational Data Store Datamarts
Systems
structured
documents
databases
Temporary
Staging
Area
APIs
staging relational dimensional
L0 L1 L2

13. real time = daily

14. Multi-dimensional
Modeling

16. aggregation browsing
slicing and dicing

17. business / analyst’s
point of view
regardless of physical schema implementation

18. Facts
measurable
fact
fact data cell
most detailed information

19. location
type
time
dimensions

20. Dimension
■ provide context for facts
■ used to filter queries or reports
■ control scope of aggregation of facts

21. Pentaho

22. Python and Data
community perception*
*as of Oct 2012

23. Scientific & Financial

24. Python

25. Data Analysis and
Extraction, Transformation, Loading
Sources Presentation
Data Governance
Technologies and Utilities

26. Scientific Data
T1[s] T2[s] T3[s] T4[s]
P1 112,68 941,67 171,01 660,48
P2 96,15 306,51 725,88 877,82
P3 313,39 189,31 41,81 428,68
P4 760,62 983,48 371,21 281,19
P5 838,56 39,27 389,42 231,12
n-dimensional array of numbers

27. Assumptions
■ data is mostly numbers
■ data is neatly organized...
■ … in one multi-dimensional array

28. Data Analysis and
Extraction, Transformation, Loading
Sources Presentation
Data Governance
Technologies and Utilities

29. Business Data

30. multiple snapshots of one source
multiple representations categories are
of same data changing

31. ❄

32. Is Python Capable?
very basic examples

33. Data Pipes with
SQLAlchemy
Data Analysis and
Extraction, Transformation, Loading
Sources Presentation
Data Governance
Technologies and Utilities

34. ■ connection: create_engine
■ schema reflection: MetaData, Table
■ expressions: select(), insert()

35. src_engine = create_engine("sqlite:///data.sqlite")
src_metadata = MetaData(bind=src_engine)
src_table = Table('data', src_metadata, autoload=True)
target_engine = create_engine("postgres://localhost/sandbox")
target_metadata = MetaData(bind=target_engine)
target_table = Table('data', target_metadata)

36. clone schema:
for column in src_table.columns:
target_table.append_column(column.copy())
target_table.create()
copy data:
insert = target_table.insert()
for row in src_table.select().execute():
insert.execute(row)

37. magic used:
metadata reflection

38. text file (CSV) to table:
reader = csv.reader(file_stream)
columns = reader.next()
for column in columns:
table.append_column(Column(column, String))
table.create()
for row in reader:
insert.execute(row)

39. Simple T from ETL
Data Analysis and
Extraction, Transformation, Loading
Sources Presentation
Data Governance
Technologies and Utilities

40. transformation = [
('fiscal_year', {"w function": int,
". field":"fiscal_year"}),
('region_code', {"4 mapping": region_map,
". field":"region"}),
('borrower_country', None),
('project_name', None),
('procurement_type', None),
('major_sector_code', {"4 mapping": sector_code_map,
". field":"major_sector"}),
('major_sector', None),
('supplier', None),
('contract_amount', {"w function": currency_to_number,
". field": 'total_contract_amount'}
]
target fields source transformations

41. Transformation
for row in source:
result = transform(row, [ transformation)
table.insert(result).execute()

42. OLAP with Cubes
Data Analysis and
Extraction, Transformation, Loading
Sources Presentation
Data Governance
Technologies and Utilities

43. Model
{
“name” = “My Model”
“description” = ....
“cubes” = [...]
“dimensions” = [...]
}
cubes dimensions
measures levels, attributes, hierarchy

44. logical
physical
❄

45. 1 load_model("model.json")
Application
∑
3 model.cube("sales")
4 workspace.browser(cube)
cubes
Aggregation Browser
backend
2 create_workspace("sql",
model,
url="sqlite:///data.sqlite")

46. browser.aggregate(o cell,
. drilldown=[9 "sector"])
drill-down

47. for row in result.table_rows(“sector”):
row.record["amount_sum"]
q row.label k row.key

48. whole cube
o cell = Cell(cube)
browser.aggregate(o cell)
Total
browser.aggregate(o cell,
drilldown=[9 “date”])
2006 2007 2008 2009 2010
✂ cut = PointCut(9 “date”, [2010])
o cell = o cell.slice(✂ cut)
browser.aggregate(o cell,
drilldown=[9 “date”])
Jan Feb Mar Apr March April May ...

49. How can Python
be Useful

50. just the Language
■ saves maintenance resources
■ shortens development time
■ saves your from going insane

51. Source Staging Area Operational Data Store Datamarts
Systems
structured
documents
databases
faster
Temporary
Staging
Area
APIs
staging relational dimensional
L0 L1 L2

52. faster advanced
Data Analysis and
Extraction, Transformation, Loading
Sources Presentation
Data Governance
Technologies and Utilities
understandable, maintainable

53. Conclusion

54. BI is about…
people
technology processes

55. don’t forget
metadata

56. Future
who is going to fix your COBOL Java tool
if you have only Python guys around?

57. is capable, let’s start

58. Thank You
[t
Twitter:
@Stiivi
DataBrewery blog:
blog.databrewery.org
Github:
github.com/Stiivi