1. Presentation plan Accessing and Retrieving SDMX data
Visualising and analysing SDMX data
Excel
R and Python
Modern BI tools (example with Qlik Sense)

2. Accessing and Retrieving SDMX data
SDMX defines a set of web services to retrieve SDMX objects and data.
Available both via SOAP and REST APIs

3. SDMX REST API – SDMX objects
RootURL: location of the REST API. For Eurostat data, it is
ObjectType: type of object requested (e.g. dataflow or datastructure)
Agency ID: The maintenance agency for the object requested (e.g. ESTAT)
ObjectID: ID of the object requested
Version: version number of object requested (e.g, 1.0 or latest)

4. Example: Retrieving information about Eurostat data
Each table in the Eurostat database corresponds to an SDMX Dataflow
Dataflow ID

5. Example: Retrieving information about Eurostat data
Query for retrieving the SDMX dataflow:
Query for retrieving the corresponding SDMX DSD:

6. SDMX REST API – SDMX data
RootURL: location of the REST API. For Eurostat data, it is
Flowref: The ID of the desired dataflow
Key: The subset of the data requested, as specified by a key (i.e. a set of values for the dimensions in the DSD). Elements of the key are separated by dots.
Parameters: Optional parameters that can further identify a subset of the data (e.g. startperiod, endperiod or lastNObservations). Parameters are always introduced by a "?" sign and separated by a "&" sign.

7. Example: Retrieving Eurostat data
I want monthly unemployment rate data from Austria from 2010 to 2013
RootURL:
Flowref: une_rt_m
Key: …..AT
Parameters: ?startperiod=2015&endperiod=2017

8. Retrieving SDMX data: caveats
In order to correctly input the key parameter, I need to know the correct codes and the correct order of the dimensions. I must look at the DSD!
If I do not want to look for a specific key, I can just use "all" instead of the dots
SDMX data comes in many formats. The default format for the REST API is the "SDMX Generic format".
If I want another format, I need to modify the query header. But for that I need a REST client…

9. Visualising and Analysing SDMX data
SDMX-ML great for data processing. But not great for understanding the data at first glance…

10. Visualising and Analysing SDMX data
Options:
Using the SDMX Converter to convert data from SDMX-ML to CSV
Opening the SDMX-ML file directly with Excel
Taking advantages of analytical libraries for parsing XML data or SDMX-ML data specifically

11. Using Excel to visualise SDMX-ML
Import the SDMX-ML file directly in Excel. Select the "As XML Table" option and ignore the pop-up warnings.
Excel will flatten the nested SDMX structure. We can then easily build a pivot table to analyse and visualise the data

12. SDMX data using R or Python
There are SDMX libraries available for both R and Python
These libraries can directly connect to SDMX Web Services import and transform SDMX data into native formats

13. SDMX data using R or Python
For R, two very good libraries are available: rsdmx and RJSDMX (the latter developed by Banca d'Italia)
For Python, there is a library called pandaSDMX (with very good online documentation)
These libraries can directly connect to SDMX Web Services import and transform SDMX data into native formats

14. SDMX data using R
rsdmx can be used to download SDMX objects and SDMX data
Advantage: unified and flexible interface (only one method to learn)
Disadvantage: unified and flexible interface (you need to know what you’re doing)

15. SDMX data using R
RJSDMX can be used to download SDMX data, but not objects
Advantage: “single-minded” approach, less SDMX knowledge expected from users
Disadvantage: fewer functionalities

16. SDMX with modern BI tools
All modern BI tools (Tableau, Qlik, Spotfire etc…) have built-in REST connectors and XML parsers
Creating a dashboard based on SDMX data is usually a matter of clicks

17. Key messages SDMX data can be retrieved via standardised web services
There are several options to visualise and analyse SDMX data