What is synapse.mdx?

synapse.mdx is a file that contains MDX (Multidimensional Expressions) code written for Azure Synapse Analytics. MDX is a query language used to retrieve and manipulate data from multidimensional databases, such as OLAP cubes. The “synapse.mdx” file typically stores these queries so they can be run, edited, or shared within a Synapse workspace.

Let's break it down

  • Synapse: Microsoft’s cloud data platform that combines big‑data and data‑warehousing capabilities.
  • MDX: A language similar to SQL but designed for querying multidimensional structures (dimensions, hierarchies, measures).
  • .mdx file: A plain‑text file with the “.mdx” extension that holds MDX statements. When the file is named “synapse.mdx,” it signals that the queries inside are intended for use in Azure Synapse.

Why does it matter?

MDX lets analysts ask complex questions about data that is organized in cubes-like “What were sales for each product category over the last quarter?” Using synapse.mdx, you can store these queries centrally, version‑control them, and run them directly against Synapse’s integrated analytics engine. This makes it easier to build dashboards, perform deep data analysis, and ensure consistent reporting across teams.

Where is it used?

  • In Azure Synapse Analytics workspaces that have linked Analysis Services or Power BI datasets.
  • By data engineers and BI developers who need to write, test, and reuse MDX queries.
  • In automated pipelines (e.g., Azure Data Factory) where the .mdx file is executed as part of a data‑processing workflow.
  • In learning environments or tutorials that demonstrate how to query multidimensional data in Synapse.

Good things about it

  • Powerful querying: MDX can navigate hierarchies, calculate percentages, and perform time‑intelligence operations that are hard in plain SQL.
  • Reusability: Storing queries in synapse.mdx lets you reuse the same logic across multiple reports or pipelines.
  • Version control friendly: As a plain‑text file, it works well with Git or other source‑control systems.
  • Integration: Works seamlessly with Synapse’s built‑in Spark, SQL, and Power BI tools, enabling a unified analytics experience.
  • Performance: When run against optimized cubes, MDX queries can be faster for certain analytical workloads than equivalent SQL joins.

Not-so-good things

  • Steep learning curve: MDX syntax is less familiar to most developers compared to SQL, requiring extra training.
  • Limited to multidimensional models: If your data is stored in relational tables only, MDX isn’t applicable.
  • Debugging can be tricky: Error messages are sometimes cryptic, making troubleshooting harder.
  • Tooling support: Fewer IDE extensions and linters exist for MDX compared to SQL, so editing may feel manual.
  • Maintenance overhead: Keeping cube designs and MDX queries in sync can become burdensome as data models evolve.