EUROSTAT – ESTP SDMX Training – April 2015

UNDERSTANDING SDMX

Introduction to a training course

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Table of Contents

1 SDMX in short..................................................................................................3

2 Why should you be interested in SDMX?...........................................................4

3 Background: origin and purpose of SDMX..........................................................5

4 The existing versions of SDMX..........................................................................8

5 The SDMX information model...........................................................................9

6 Uptake of SDMX within Domains....................................................................11

7 How to know more: SDMX Tutorials................................................................13

8 How to know more: the SDMX User Guide.......................................................13

Compiled by Marco Pellegrino (Eurostat)

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What is SDMX
This document provides some background information on the SDMX Initiative, the issues
which SDMX addresses and the areas in which SDMX is playing a role today.

1 SDMX in short
SDMX provides support for things that are important to statisticians and are often difficult
for them to achieve, and it enables tools to be developed to provide support for these
things. It does this by providing standard, well-designed formats for holding all of the
elements involved in the statistical process and linking them all together in a clear model.
The result is an approach that maximises the amount of metadata and statistical context
information that can be passed through to statistical clients, maximises the possibility of
relating statistics from similar or different sources, and allows for the automation of
processes that are often difficult and costly to manage otherwise.
SDMX, with its standards for metadata and data representation, provides a basis for the
development of common tools that can be used by all statistical organisations to improve
their dissemination-related activities.
SDMX offers a wide variety of technical tools, and these – like any tools – produce positive
results if used well. There is already an impressive array of excellent tools for presenting and
working with SDMX data and metadata. But SDMX is not only a standard for data and
metadata exchange; it is also a standard for data discovery using web services. Such web-
based services enable data and metadata query, visualisation, automated database creation
and data loading, metadata query and retrieval from a metadata repository, the of linking
data and metadata. Tools exist today that enable the querying of a database, or even file of
data as if it is a database, and creation of tables, charts, and graphs from the results of the
query. Tools exist to create a database directly from a Data Structure, load the Data Set, and
respond to queries for data. Tools also exist to create and maintain SDMX structural
metadata and to share this with others.
SDMX also offers a set of guidelines regarding the application of harmonised statistical
concepts and codes to data sets, and how these can be represented. Other guidelines
address the classification of statistical data and domains, and the harmonization of relevant
terminology. Additionally, SDMX represents a framework for the process of harmonization
within domains.
In short, SDMX is a model for describing statistical data and metadata, and formats for
presenting them with almost unlimited capability for linking the information and adding
value in terms of adding quality information, explanatory material, and other forms of aids
to interpretation.
SDMX organises and classifies statistical metadata and data to support search and discovery,
to link related material, and to link all data to its metadata and to its sources and providers.
As it structures the statistical material in well-defined standard formats, SDMX provides an
excellent basis for automated tools to operate on data and metadata – to populate
websites, to provide varied and dynamic data presentations, to resolve queries and find

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specific details, or to convert material into other formats such as those used by spreadsheets
and common statistical tools. There is no better way to support a powerful and flexible
dissemination environment for statistical data than to organise that data into SDMX
structures.

2 Why should you be interested in SDMX?

Why should statisticians and National Statistical Offices be interested in SDMX? What does
SDMX offer that makes it useful? How will SDMX help statisticians in doing their day-to-day
work of producing statistics that are useful and meaningful to clients?
Statisticians need to describe, disseminate, share, relate, and manage their statistical
information and processes. They need to publish statistics in ways that convey the full
meaning and context to clients, that make quality standards and qualifications clear, and
that allow the statistics to be compared and related to other available information. And they
would like these products to be produced easily and flexibly with a minimum technical
effort. SDMX is focused directly on these requirements. SDMX stands for “Statistical Data
and Metadata eXchange” but really SDMX is about much more than pure exchange.
To facilitate data and metadata exchange, one needs:
- a rich and mature model for describing statistical data and metadata;
- comprehensive formats for representing data and metadata, e.g. table structures,
related sets of statistical data, and the various quality and other attributes that may
be applied to the data;
- well-defined way of making this information available for sharing and access by
interested parties;
- tools (operating with the model and the formats/standards) to enable integration,
simplify the presentation process, facilitate comparisons of metadata and data, and
support collaboration around the data.
This is what SDMX provides and enables. SDMX is a good model for statistical presentation
and dissemination activities; the various formats and structures it uses are effective and fit-
for-purpose, and an increasing number and tools to work with SDMX-formatted data and
metadata are available or becoming available.
Moreover, SDMX is owned and being embraced by the official statistics community – the
various national and international statistical organisations that are committed to extracting
the maximum value out of statistical information. SDMX has been shaped by their needs
and its evolution will continue to be driven by these needs. There is a thriving community
from these national and international organisations that meets in regular meetings and
workshops and in online forums to share ideas, approaches, tools, and requirements.
As an example of a possible use case for SDMX, one could envisage an organisation that:
- produces and publishes statistics accessed by important national clients and by
international organisations;
- receives questions both about the origin, production, and quality of the statistics
and about comparisons with similar statistics from other countries;
- wishes to make available graphical and flexible presentations of the statistics;

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- wishes to automate the process of meeting these requirements to the greatest

extent possible to reduce ongoing costs and effort.
SDMX is designed to meet these requirements. It is a standard for machine-processable
metadata that carries clear definitions and quality and commentary information, and is an
ideal enabler for tools that support self-service and flexibility in presentation.

3 Background: origin and purpose of SDMX

The Statistical Data and Metadata eXchange (SDMX) initiative was launched in 2001 by seven
organisations working on statistics at the international level: the Bank for International
Settlements (BIS), the European Central Bank (ECB), Eurostat, the International Monetary
Fund (IMF), the Organisation for Economic Co-operation and Development (OECD), the
United Nations Statistical Division (UNSD) and the World Bank. These seven organisations
act as the sponsors of SDMX. They created an initiative with a governing sponsors
committee, and a secretariat function to execute the work programme.
The issues can be briefly characterized as follows:
 Statistical collection, processing, and exchange is time-consuming and resource-
intensive
 Various international and national organisations have individual approaches for their
constituencies
 Uncertainties (in 2001) about how to proceed with new technologies (XML, web
services etc.)
In 2001, the SDMX Initiative stated that it would address these issues:
 By focusing on business practices in the field of statistical information
 By identifying more efficient processes for exchange and sharing of data and
metadata using modern technology
It was further stated that: “New standards should take advantage of the new web-based
technologies and the expertise of those working on the business requirements and IT
support for the collection, compilation, and dissemination of statistical information.”
The stated aim of SDMX was to develop and use more efficient processes for exchange and
sharing of statistical data and metadata among international organisations and their
member countries. To achieve this goal, SDMX provides standard formats for data and
metadata, together with content guidelines and an IT architecture for exchange of data and
metadata. Organisations are free to make use of whichever elements of SDMX are most
appropriate in a given case.
With the Internet and the world-wide web, the electronic exchange and sharing of data has
become easier and more common, but the exchange has often taken place in an ad hoc
manner using all kinds of formats and non-standard concepts. This creates the need for
common standards and guidelines to enable more efficient processes for exchange and
sharing of statistical data and metadata. As statistical data exchange takes place
continuously, the gains to be realised from adopting common approaches are considerable
both for data providers and data users.
SDMX aims to ensure that metadata always come along with the data, making the
information immediately understandable and useful. For this reason, the SDMX standards
and guidelines deal with both data and metadata. Common standards and guidelines

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followed by all players not only help to give easy access to statistical data, wherever these
data may be and without demanding prior agreement between two partners, but they also
facilitate access to metadata that make the data more comparable, more meaningful and
generally more usable.
Thus, the goals of the SDMX initiative were broadly agreed across the sponsoring
organizations, and within the official statistics community generally. “Official statistics” are
the data which is collected and disseminated by a set of governmental and international
organizations to provide the factual basis for making policy and supporting research. Some
countries have a “national statistical office” (NSO) while others may have several
governmental organizations which are charged with collecting statistical data for
governmental use. Most countries also have central banks or similar organizations which
collect and disseminate financial and economic data. Typically, several national government
organizations have a statistical function (ministries of education, justice, labour, etc.)
These national organizations typically report their statistics to a set of supra-national
organizations, representing either regions of the globe (examples include Eurostat and the
European Central Bank) or domains (examples include the World Health Organization, the
Food and Agriculture Organization, UNESCO, and the World Bank). Many of these
organizations belong to the UN, or are treaty organizations. All of these organizations
exchange, report, and disseminate data in a chain which can be understood as starting at the
lowest level within each country, and resulting in high-level data sets which are
“aggregated” as they move through various levels to reach the international level.
The system of official statistics is this network of reported data, according to legal
requirements or other types of agreements. There are several important meetings,
conferences, and initiatives within this system, so that all organizations adopt similar
approaches and techniques, and to coordinate reporting: the Conference of European
Statisticians is an important meeting, as is the United Nations Statistical Commission
meeting. Ultimately the goal is to measure important phenomenon occurring in the world,
and to report the data to policy makers, students, journalists, and other users to help inform
their activity. The data is “official” because it comes with the reputation of the world’s
governments and international institutions behind it.
It is important to understand that there were some firm foundations on which SDMX was
building:
1. An existing standard for exchanging statistical time-series data, known as
GESMES/TS, was already in use among several of the sponsor organizations and
their national-level counterparties. This was not based on modern Web technologies
such as XML, but used the older UN/EDIFACT syntax.
2. The work on the “metadata common vocabulary” was based on many years of
harmonization work within the community, notably Eurostat’s Concept and
Definitions Database (CODED) and the OECD Glossary of Terms.
The formation of the SDMX Initiative can be understood as a recognition by the sponsor
organizations that working together to address these issues, and that coordinating business
approaches using modern, standards-based technology, was the best way forward. In one
sense, SDMX evolved from earlier work, but indicated the increased commitment the
sponsors had toward reaching its goals. It also represents a coming-together of efforts
around harmonizing statistical content and terminology, and for deploying technology to
support statistical processes.

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Over time, the work of the SDMX Initiative has expanded, both in terms of content-oriented
work products and technical ones.
The SDMX Initiative decided early on to position the content-oriented work and the work on
technology and standards in a fashion which made these strains of work separate but
complimentary. The content-oriented work led to the development of the SDMX Content-
Oriented Guidelines, while the technical work resulted in the SDMX Technical Specifications.
There were several reasons for taking this approach. It reflected the realization that
technical specifications must be very precise and detailed in order to allow for automation of
statistical exchanges – the programming of computers relies on having very specific rules
about how applications communicate, otherwise the communication fails. The SDMX
technical standards in one sense function as exchange protocols for machine-to-machine
communications (similar to HTTP, for example, but with a focus on specifically statistical
exchanges).
Statistical content and terminology issues are very different – they are the subject to
interpretation and analysis by trained statisticians. Thus, the technology specifications
formed a basis for supporting work on the content side, but in fact are a very different type
of work product. It is easiest to see this in the fact that the SDMX Content-Oriented
Guidelines are guidelines, to help suggest approaches to people in their statistical work,
while the SDMX Technical Specifications are specifications - rules for developing conforming
computer applications.
Another reason for this separation is that the technical specifications and content guidelines
were expected to be maintained at different rates – once stable, technical specifications
tend to be updated less frequently. Also, the reasons for making updates and changes in
each area have no dependency between them, so it made sense to separate them. This is
reflected in the fact that the technical specifications are submitted and published through
the International Standards Organization (ISO), who publish many IT-related standards in
various domains, while the content-oriented guidelines are not submitted to ISO, but are
maintained by the SDMX Initiative itself. This allows for updates of the content-oriented
guidelines on an on-going basis.
A third reason for the separation of the SDMX Technical Standards and the SDMX Content-
Oriented Guidelines is that – because they are a technological foundation for exchanging any
statistics – the technical specifications are applicable outside the domain of official statistics,
while the content-oriented guidelines are specifically designed to be useful within that
context (although they might also be useful outside that community, possibly).
This coordinated-but-separate positioning of the two threads of work has proven to be very
useful, too, because often statisticians and economists do not have deep expertise in IT, and
technologists do not have deep expertise in statistics. SDMX helps to define the point where
the two sets of expertise need to coordinate, to effectively use IT within statistical exchanges
and processes.
Within the content-oriented work, there is a set of work products, The Content Oriented
Guidelines, and 5 annexes:
1. Cross-Domain Concepts
2. Cross-Domain Code lists
3. Statistical Subject-Matter Domains
4. Metadata Common Vocabulary

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5. SDMX-ML for the Content-Oriented Guidelines (Concepts, Code Lists, Category

Scheme)
The first draft of the Content-Oriented Guidelines was released for public review in March
2006, and a consolidated version was released for public review in February 2008. The full
release of the Content-Oriented Guidelines, which have been extensively revised to take
account of comments received, took place in January 2009.

4 The existing versions of SDMX

The Version 1.0 SDMX standards were approved by the sponsors in September 2004 and
accepted as an ISO technical specification (ISO/TS 17369) in April 2005.
In November 2005, the sponsors approved Version 2.0 of the SDMX standards, which are
fully compatible with Version 1.0 but in addition provide for the exchange of reference
(explanatory) metadata, and include the registry interface specification.
The SDMX Technical Specifications are now in version 2.1, but both version 1.0 and version
2.0 were implemented.
The 1.0 version of the specifications has a relatively limited coverage – a model for data
formats and their structures, along with XML and UN/EDIFACT formats for exchanging these.
The UN/EDIFACT format was backward-compatible with GESMES/TS; the XML formats were
new. There was also some support provided for SDMX-based Web services: an XML query
document, and a set of guidelines about the use of other related Web-services standards
(SOAP and WSDL).
The 2.0 version of the technical specifications had a greatly-expanded scope. The model was
extended to include “reference metadata” as a way of structuring and formatting metadata
related to data quality frameworks, methodological metadata, and other types of “footnote”
metadata. Thus, XML formats for reference metadata were added. Further, a set of
standard interfaces in XML for interactions with a SDMX Registry were added, for
cataloguing the location of data and reference metadata across the Internet or within an
organization, and for maintaining and retrieving structural metadata.
In version 2.1, many features of 2.0 have been improved, and the Web-services
recommendations have been expanded to include a RESTful interface, standard functions,
and error messages. Now, it is possible to develop generically interoperable applications
based on the SDMX standards. Further, the various XML data formats have been simplified
based on implementation experience with version 2.0.
For all types of work products, there have been internal reviews within the SDMX
community, and also public review of the guidelines and standards.
SDMX was approved as an ISO technical specification (ISO/TS 17369) in 2005 and has
become ISO international standard (ISO/IS 17369) in 2013.
In March 2007, the sponsoring institutions signed a Memorandum of Understanding (MoU),
which is intended to set out the arrangements for a durable collaboration by the sponsors
on all aspects of SDMX. The MoU explicitly excludes the formation of any legal entity or
common budget for SDMX; each sponsoring institution and its member countries will
continue to use its existing procedures to agree on arrangements for transmission and
publication of statistics.

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In the conclusions of the 39th Session of the UN Statistical Commission (New York, February
2008), SDMX was recognised and supported as "the preferred standard for exchange and
sharing of data and metadata in the global statistical community" . This acceptance of SDMX
at UN level is a major step forward towards the broader use of SDMX at world-wide level.

5 The SDMX information model

SDMX deals with things statisticians understand well – metadata about concepts,
classifications, and table structures, and statistical data related to this metadata. Most of its
terminologies are familiar to statisticians although there are a few new terms. Its “artefacts”
– the objects it works with and that users must build – are things that statisticians work with
on a daily basis: concept definitions, statistical classifications, table structures, data values
and data tables, and data attributes like quality information or observation statuses.
The formats SDMX uses to represent these artefacts are possibly unfamiliar. The SDMX
formats are based on XML – ideal for machine automation – but statisticians do not need to
work at this level: they work with the data and metadata content, while the tools deal with
the detail of the format.
The SDMX model for disseminating data is shown in the figure below.

Data Category
Structure Scheme
Definition

Data Set Data Flow

Category

Provision
Agreement
Data Provider

The key elements of the model are:

Data Flows. Examples of data flows are Quarterly National Accounts statistics and Quarterly
(or monthly) Unemployment statistics that many statistical offices produce, along with many
other similar regularly-repeated collections. The idea of a data flow is a sequence or series

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of regularly-repeated data sets coming for different time periods, or from different countries
(or both), or being repeated over some other dimension. If we consider National Accounts,
a table typically contains a dimension of measures (Final consumption expenditure –
Government and Households, Gross fixed capital formation, etc), a dimension of values
(Value, % changed over quarter, % changed over year, etc) and perhaps a Seasonal
Adjustment dimension (Original, Seasonally adjusted, Trend). A Data Flow includes
additional dimensions of Time (Q1/2011, Q2/2011, Q3/2011) and perhaps Country (for
international organisations such as Eurostat and IMF), for which sub-sets of the data come at
different times or from different providers. Thinking of it as a single entity (and holding
metadata at the level of this single entity) enables SDMX (and tools working with SDMX) to
view all the component data sets as part of this hyper-cube, to understand their
commonalities, and to provide very powerful and flexible presentations of the data.
Data Structure Definition. This is the formal definition of the hyper-cube structure for a
Data Flow. It defines the hyper-cube in terms of its dimensions, linking them to Concepts
and Classifications (which SDMX calls Codelists). It also provides for Attribute information
that can provide additional information such as statistical quality, observation status, or
other footnote-type information at cell level, at table level, or at intermediate levels.
Data Sets. A Data Set is one of the individual tables that make up a flow. A particular data
set might be a National Accounts for France for Q2 2013, for example. A Data Set contains
data for some sub-cube of the hyper-cube described by the Data Structure Definition. The
Data Flow is made up of its member Data Sets (including those that are still to come in the
future).
Category Schemes and Categories. These provided indexes for the data flows to allow
searching and discovery. Categories can be nested within a Category Scheme and there can
be several alternative Category Schemes indexing data flows. A category scheme might be
based on a catalogue of publications, or on a dictionary of statistical terms, or on some index
of terms in common use amongst a particular set of clients.
Data Providers and Provision Agreements. These are the formal mechanisms for capturing
agreements to provide data, along with contact details, in a data exchange environment.
But they also hold information that is extremely useful in supporting a statistical
dissemination website. They provide the basis for automated release calendars and for
automated presentation of contact information for queries.
SDMX specifies web services for client programs to query for and retrieve data and
metadata directly, it includes mechanisms that support management of complex
classifications, classification versions, and related and similar classifications. There are ways
to link data flows whose structures contain common dimensions so that tools may harvest
this commonality to produce more powerful presentations. There are ways to describe all
the elements that go to make up a report or publication to support automatic generation.
And there is much more – more that you will discover as you use SDMX.

6 Uptake of SDMX within Domains

SDMX has become very widely used within the world of official statistics, so much so that it
is difficult to form a comprehensive list of users. This section attempts to characterize the
current users of SDMX – a group that will likely grow not only in terms of numbers, but also

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in terms of the breadth of applications. A few possibilities here are suggested at the end of
this section.
If we are to look at the most common uses of SDMX, there are two:
1. The use of SDMX as a reporting and collection format, which is prevalent within the
central banking community (as a result of the earlier implementation of GESMES/TS,
now SDMX-EDI) and among the statistical agencies in Europe (also users of GESMES
historically, but implementation is now increasingly driven by such projects as
Eurostat’s Census Hub);
2. Dissemination of statistical data from websites.
The second application is one which we see in a broad range of institutions, including central
banks (ECB and European System of Central Banks, BIS, U.S. Federal Reserve Board and New
York Federal Reserve, among others), other sponsoring institutions (IMF, World Bank, OECD,
etc.), and national statistical agencies (INEGI in Mexico, Statistics New Zealand, Australian
Bureau of Statistics, statistics offices in the European Statistical System etc.)
A less-common but growing use of SDMX is as the basis for data warehouses and other
forms of data management. Perhaps the best example of this is the European Central Bank,
which has created all of its internal data warehouses around the SDMX Information Model,
and has realized many benefits from this. They are by no means the only organization
looking at this type of implementation, however – many other organizations are using SDMX
to manage not only their statistical data, but also to create metadata repositories, and to
integrate their metadata and data.
If we look at which statistical domains have been or are becoming major adopters of SDMX,
the list would be something like this (in no particular order):
 Census and Demography
 Education
 Financial and Monetary Indicators
 Economic Indicators
 National Accounts
 Labour
 Food and Agriculture, including fisheries
 Epidemiology
 Transport
 Data Quality
 Development Indicators

It is easy to see that this is a broad and cross-cutting set of statistical domains – in fact, there
are probably very few domains in which SDMX is not being used in some fashion today, and
the above list is intended as an indication of the breadth of the uptake..
SDMX was officially endorsed first within the European statistical system, and then by the
UN Statistical Commission. These endorsements were powerful incentives for organizations
to use SDMX, and the result has been widespread adoption. There are no major competing
standards, which has saved the world of statistics from a phenomenon which has slowed the
uptake of standards in some other communities.
Additionally, a strong culture of open-source and free tools development has emerged,
helping to make the adoption of SDMX easier. This has come both from within the sponsors'

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community and without, and is supplemented by an increasing number of tools coming from
commercial vendors as well.
To learn more about available SDMX tools, the best place is to consult the SDMX website at
www.sdmx.org.
The Open Data Foundation hosts the SDMX User Forum in collaboration with the sponsors,
providing a place where the community can interact online, and Eurostat’s CIRCABC website
provides many types of resources, from training videos to student guides. Many
organizations offer SDMX in-person training for different levels of users. The best single
point of entry is of course the SDMX website itself.
Looking forward, SDMX is increasingly coming into use: Google is using SDMX as a source of
data for its Data Explorer; there is now a global registry so that all SDMX data and metadata
sources can be easily found; more and more statistical domains are using SDMX for data
exchange on a world basis. Furthermore, we see the strong possibility that the world of
corporate statistics may realize the utility of having a strong standards basis around the vast
amounts of data collected today to support business intelligence applications.

7 How to know more: SDMX Tutorials

SDMX Tutorials have been developed by Eurostat to promote the use and implementation of
SDMX standards and guidelines: a set of e-learning videos, each accompanied by a “student
book” and a self-test file, are available at https://webgate.ec.europa.eu/fpfis/mwikis/sdmx.
This is the list of currently available videos (updates are foreseen in the course of 2015):
1. Welcome to SDMX - Why and how SDMX can help you
2. Introduction to SDMX - Outline of SDMX in terms of its history and main components
3. SDMX Information Model - A description of the Information Model and its main objects
4. Data Structure Definition - Explains how data is structured in SDMX
5. Metadata Structure Definition - Explains how SDMX structures reference metadata
6. SDMX-ML Messages - Describes the XML based transmission format supported by SDMX
7. XML based technologies used in SDMX
8. SDMX Architecture for data sharing - How SDMX can facilitate data sharing using
different IT architectures

8 How to know more: the SDMX User Guide

Two user guides, one referring to SDMX 2.0 and one referring to SDMX 2.1, are available on
the SDMX web site at http://sdmx.org/?page_id=38.
The new SDMX 2.1 User Guide aims at providing guidance to users of the version 2.1 of the
Technical Specification, released in April 2011. As version 2.1 of SDMX contains several
innovative parts (such as web services guidelines, new data messages, new code lists and
metadata management) this new release intends to document how the new standard can be
used to fulfil the most typical use cases and scenarios for data and metadata exchange.

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The principal intention is helping organisations and individuals to determine how best to use
SDMX in order to help them to improve the statistical production process. In order to
achieve this objective, examples are taken from real implementation scenarios that enable
the reader to understand the scope of the SDMX standards and guidelines in terms of the
activities required in order to collect, process, and publish statistical data and reference
metadata.

Table of contents of the SDMX 2.1 User Guide

Chapter Content
1. Introduction Objective, Scope, and Structure of the Guide
2. What is SDMX Background, sponsors, users, use cases, industry sectors. Brief
overview of the technical and content standards, tools, where
to find more information and help.
3. Scenario, Use Cases, This is based on the SDMX Information Model. The chapter
and Example relates the Information Model to the real activities of
reporting, processing, and dissemination of statistics.
4. Data and Metadata Explanation of the structural components of a Data Structure
Creation and Reporting Definition and a Metadata Structure Definition, and of the
Data Set and Metadata Set. How these are used in data and
metadata reporting scenarios.
5. Data Bases and SDMX Explanation of the relationship between the tables in a
database and a Data Structure Definition and how the DSD can
be used to create these tables. Explanation on how to open a
database to SDMX web services.
6. Data and Structure The scope of the data query and the map to the Information
Query Model. Scope of the REST and SOAP queries. Useful tips on
what type of queries to support.
7. Metadata Repository Typical requirements for metadata (quality frameworks,
and Linking Data to linking to disseminated data).
Metadata Architecture for a metadata repository to enable data and
metadata to be combined in a dissemination environment.
8. SDMX Registry Role of the Registry in statistical data and metadata reporting
and dissemination systems.
Difference between the content and functions of a Registry
and a non-registry based structural metadata repository.
Content and role of the Registry in terms of:
 Structural metadata maintenance and query
 Registration of data and metadata sources
 SDMX Registry Services
 Web services that can make use of SDMX Registry Services
9. Architecture for an Brings all of the components together in an overall
SDMX System architecture comprising:
 Data and metadata persistence and interfaces
 Server side middle tier brokering of requests for data and
metadata, data loading, validation, transformation
 Client side tier of:
o Structural metadata maintenance

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Chapter Content
o Data query and visualization
o Validation, transformation
10.Community The community may be at the level of an organization or in
Management the context of the wider community of organizations. Topics
are:
 Role of a Global Registry
o Maintenance agency maintenance
o Common concepts
o Community structural metadata
o Maintenance of Dimension and Attribute roles
 Hosting of a shared Registry
 Data provider maintenance

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