XmlaEdit

XML for Analysis, commonly abbreviated as Xmla, is a SOAP-based standard for accessing and managing OLAP (online analytical processing) data sources and data mining services. It defines a uniform protocol and message formats that allow clients to discover metadata, execute analytical commands, and receive results in XML. The goal is to enable interoperable BI (business intelligence) tooling across different servers and platforms, rather than tying users to a single vendor’s stack. In practice, Xmla is most closely associated with Microsoft’s suite of BI products, especially SQL Server Analysis Services, but it has been implemented by other vendors to varying degrees, making it a useful if aging cornerstone of enterprise analytics. XML for Analysis is the term most practitioners use to describe both the protocol and the set of capabilities it exposes.

History

Xmla emerged in the early 2000s as a joint standard developed by the OLAP Council and major industry players, including Microsoft. It was designed to standardize how clients communicate with OLAP servers and data mining engines, replacing bespoke interfaces with a single, interoperable API. Over time, the specification evolved through updates that expanded metadata capabilities and refined the execution model. The result was a durable framework that could support enterprise-scale analytics across heterogeneous environments, even as new data technologies and interfaces appeared. The standard remains in use in many large organizations, often interfacing with legacy BI investments, even as some teams migrate toward RESTful or cloud-native options for new projects. See discussions surrounding driving standards across platforms and cloud services in the context of OLAP ecosystems and Business intelligence.

Architecture and core concepts

Protocol and core operations

Xmla relies on HTTP as the transport and uses SOAP envelopes for both requests and responses. The two primary operations are: - Discover: used to obtain metadata and schema information about the data source, catalogs, cubes, dimensions, measures, and other objects. Discover interacts with metadata through predefined schemas, such as the MDSCHEMA family of rowsets. - Execute: used to run analytical commands against the server, typically written in MDX (Multidimensional Expressions) for OLAP queries, or DMX (Data Mining Extensions) for data mining tasks.

Responses are returned as XML, containing either metadata rowsets or query results. This architecture makes Xmla language-agnostic at the client level and capable of supporting a wide range of BI tools, including traditional desktop clients and server-based reporting solutions. See MDX for the query language commonly used with the Execute operation.

Data model and metadata

Xmla exposes a hierarchical data model that includes: - DataSource: the physical or logical connection to a data source. - Catalog: a collection of related objects within a data source. - Cube: the primary analytical structure built from facts and measures. - Dimensions, hierarchies, and members: the structural elements of cubes that define how data is sliced and aggregated.

To learn about the kind of metadata Xmla can reveal, readers can explore the various MDSCHEMA schemas invoked through Discover, which describe objects like cubes, dimensions, measures, and the relationships between them. See OLAP for the broader data-analytic context and MDX for the query language that drives many of these interactions.

Query languages and data mining

The Execute operation typically carries an MDX statement to retrieve measures, coordinates, and calculated members from a cube. For data mining tasks, DMX can be used to define models and queries against mining structures within the same Xmla framework. The separation of discovery (metadata) and execution (queries) helps keep tooling flexible, but it also makes Xmla feel verbose and heavy compared to more modern RESTful interfaces. See DMX and MDX for deeper dives into these languages.

Interoperability and security

Xmla’s design emphasizes interoperability across vendors, which makes it attractive for large, diverse BI ecosystems. Security is handled through the underlying transport (often HTTPS) and the server’s authentication and authorization mechanisms. In practice, securing an Xmla endpoint involves standard network security measures plus proper user permissions on the OLAP server. See SOAP for the protocol layer and XML for the data-exchange format.

Adoption and use cases

Xmla has been a workhorse in enterprise BI where stability, cross-vendor compatibility, and the ability to integrate old and new tools matter. It is commonly used to: - Connect clients like Microsoft Excel or third-party BI tools to SSAS or similar OLAP servers. - Enable centralized metadata management and governance through standardized Discover operations. - Support automated reporting and data extraction workflows that rely on MDX-based analysis.

As enterprises adopt cloud data warehouses and REST-based analytics, Xmla’s role has evolved from being the sole access path to being one of several available interfaces. Some organizations maintain Xmla endpoints to preserve compatibility with legacy tools and dashboards, while newer projects may favor REST/JSON or vendor-specific APIs for better performance and simpler development models. See REST and OData for alternatives gaining traction in modern architectures.

Controversies and debates

From a market-friction perspective, Xmla sits at the intersection of openness and incumbent dominance. On one side, the standard advances interoperability and reduces vendor lock-in, enabling shops to mix and match analytics engines and clients without rewriting core logic. Proponents point to this openness as a shield against monopolistic coupling and a accelerant of competitive markets for BI software. On the other hand, Xmla’s real-world usage has often been dominated by a few big players, especially where SSAS is involved, which can create practical lock-in despite a standards-based façade. Critics sometimes argue that the standard lags behind modern, API-first approaches and that its SOAP-based payloads add overhead compared with lean REST/JSON interfaces. Supporters respond that for large, audited enterprise environments, the reliability and mature tooling around Xmla still deliver real-world value.

Some readers may encounter critiques framed in broader debates about technology standards and market structure. Proponents of a more market-driven, interoperable ecosystem view Xmla as a pragmatic compromise: a stable, well-understood protocol that supports diverse data sources and long-lived workloads. Critics who emphasize speed and developer experience may favor newer interfaces that reduce boilerplate and latency. When evaluating Xmla, practitioners typically weigh the cost of maintaining older interfaces against the benefits of cross-vendor compatibility and the depth of metadata available through Discover operations. In this context, the argument that embracing a long-standing standard inherently stifles innovation is often overstated; the practical gains in reliability, governance, and predictable performance can outweigh the perceived rigidity.