What is sigmacomputing.mdx?

sigmacomputing.mdx is a file that contains MDX (Multidimensional Expressions) code specifically written for the Sigma Computing platform. MDX is a query language used to retrieve and manipulate data from OLAP (Online Analytical Processing) cubes. The .mdx file lets Sigma users define complex calculations, hierarchies, and data slices that can be reused inside their Sigma workbooks and dashboards.

Let's break it down

  • Sigma Computing - a cloud‑based analytics tool that lets users explore data with a spreadsheet‑like interface.
  • MDX - a language similar to SQL but designed for multidimensional data (cubes). It lets you ask questions like “show me sales for the last three quarters by region.”
  • .mdx file - a plain‑text file that stores those MDX statements. In Sigma, the file is named sigmacomputing.mdx to indicate it belongs to the Sigma environment.
  • How it works - When you import or reference sigmacomputing.mdx, Sigma reads the MDX code, runs it against the connected data warehouse or cube, and returns the results as tables or visualizations.

Why does it matter?

  • Powerful analysis - MDX can perform calculations across multiple dimensions (time, geography, product) that are hard to do with simple SQL.
  • Reusability - Storing queries in a .mdx file means you can reuse the same logic across many dashboards without rewriting code.
  • Performance - Well‑written MDX queries can push computation to the database engine, reducing the amount of data transferred to Sigma and speeding up reports.
  • Collaboration - Teams can share the sigmacomputing.mdx file, ensuring everyone works from the same definitions and metrics.

Where is it used?

  • In Sigma Computing workbooks that connect to OLAP cubes or data warehouses supporting MDX (e.g., Snowflake, Redshift with external cube layers).
  • In data‑engineering pipelines where analysts version‑control MDX scripts alongside other code.
  • In corporate reporting environments that need multi‑dimensional analysis, such as finance, sales, and supply‑chain dashboards.
  • Occasionally in training or demo projects to showcase advanced Sigma features.

Good things about it

  • Flexibility - Allows complex, multi‑dimensional calculations that go beyond basic spreadsheet formulas.
  • Portability - As a plain‑text file, it can be stored in Git, shared via email, or uploaded to Sigma with ease.
  • Performance optimization - Lets the underlying database do heavy lifting, often resulting in faster query times.
  • Consistency - Centralizes business logic, reducing the risk of “spreadsheet drift” where different users calculate metrics differently.
  • Integration - Works seamlessly with Sigma’s visual interface, letting non‑technical users explore results without seeing the MDX code.

Not-so-good things

  • Learning curve - MDX is less familiar than SQL; beginners may find its syntax and concepts (tuples, members, hierarchies) confusing.
  • Debugging difficulty - Errors in MDX can be cryptic, and Sigma’s UI may not always surface detailed error messages.
  • Limited support - Not all data sources support MDX; you may need an additional cube layer or specific connectors.
  • Version control overhead - Managing many .mdx files can become cumbersome without a solid Git workflow.
  • Potential over‑engineering - For simple analyses, writing MDX may be overkill compared to straightforward SQL or Sigma’s built‑in functions.