Migrating SDW API queries to EDP API queries
The SDW web services API will be repointed to the ECB Data Portal API as of the official go-live. User requests will be automatically redirected for at least a year from https://sdw-wrest.ecb.europa.eu to https://data-api.ecb.europa.eu. API request syntax will remain unchanged, as data structures and codifications remain the same.
Please proactively change your reference from SDW API to EDP API as the redirections that are provided will be running for limited period of time and could not work for all applications.
What are the ECB Data Portal web services?
The ECB SDMX 2.1 RESTful web service offers programmatic access to the statistical data and metadata disseminated via the ECB Data Portal.
The service offers two modes of operation. Data retrieval mode enables you to access the specific data you want to retrieve. Data discovery mode uses a metadata-driven approach to enable you to discover data exposed by the web service.
The web service complies with the SDMX 2.1 RESTful web service specification.
Concepts and codelists
To make sense of some statistical data, we need to know the Concepts associated with them. A Concept is a value or quality that can be ascribed to data in order to give them meaning. For example, the figure
1.2953 is meaningless on its own. However, it becomes meaningful when we know that it represents the exchange rate of the US dollar against the euro on 23 November 2006.
Some Concepts may be free text (e.g. a comment about a particular Observation value), but others derive from controlled vocabulary lists known as Codelists (e.g. a list of countries).
Data Structure Definitions
There are two types of Concepts: Dimensions and Attributes.
Dimensions can be used in combination to specifically identify statistical data. Dimensions are the measured aspects of a phenomenon (e.g. time, place, product, customer) which, in unique combinations, specifically identify statistical data.
Attributes, on the other hand, do not help in identifying statistical data; they are characteristics that add useful qualitative information, such as the number of decimal places to which the data are measured.
All the Concepts that describe a particular domain (e.g. exchange rates or inflation) are grouped in a Data Structure Definition (DSD). Within this structural context, Dimensions and Attributes are known as Components.
Statistical data can be grouped together at different levels. In SDMX, the measurement of a phenomenon (e.g. the figure
1.2953 mentioned above) is known as an Observation. A grouping of several Observations is called a Dataset.
Intermediate grouping of the data is also possible. For example, a Time series groups measurements of a phenomenon taken at regular intervals (e.g. the daily exchange rate of the US dollar against the euro).
A Cross-section groups a collection of Observations made at the same point in time (e.g. the values of the US dollar, the Japanese yen and the Swiss franc against the euro on a particular date). Intermediate groupings are optional, and you may instead decide to access a simple Flat list of Observations.
Dataflows represent groupings of related data from a particular statistical domain (e.g. balance of payments). They provide a reference to the DSD that applies for a particular domain, thereby indicating how the data for that domain will look.
Although this short introduction can only provide limited details about the SDMX information model, the information above should be sufficient for understanding the basics of this web service. For additional information, please refer to the SDMX documentation.