Data architecture consultancy

Building data platforms on Microsoft.

Dimensional models, Azure Data Factory pipelines, Microsoft Fabric lakehouses and Power BI reports, for companies that need their data warehouse to work. Fifteen years on SQL Server.

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Power BI report page over a Microsoft Fabric lakehouse: rating progress, results by year and accuracy
Page one of the Chess Data Platform report in Power BI, over a Fabric lakehouse.
15+years on SQL Server, from on-prem estates to Azure and Fabric
6 moaverage initial contract
36+ moaverage engagement once extended
100%renewal rate with top clients

Contracts start short and get extended.

Services

What we do

New platforms, reporting estates and migrations. In each case the deliverable is a model that also answers the next question, pipelines that can be rerun safely, and documentation that lets the client’s own team operate the result.

Platform architecture

Medallion lakehouses and warehouses on Microsoft Fabric, Azure Data Factory and Azure SQL.

  • Kimball star schemas: conformed dimensions, surrogate keys, grain chosen per fact
  • Bronze, silver and gold layers with clear contracts between them
  • Orchestration that pauses what it started and logs every load

Analytics and BI

Power BI semantic models and reports built for the questions the business actually asks.

  • Semantic models and reports as code, deployed by API
  • DAX checked by query before a visual is built
  • Direct Lake and Import, chosen for the workload

Modernization

Moving SSIS, SSRS and on-prem SQL Server estates to Azure without losing what worked.

  • Migration paths that keep reports live throughout
  • Least-privilege identities and secrets in Key Vault from day one
  • Budget alerts, auto-pause and pause-by-default from the start

Work

Everything below runs on this site. The Chess Data Platform is a complete build on Microsoft Fabric, documented layer by layer.

Microsoft Fabric workspace lineage: connections, lakehouses, pipelines, notebooks, semantic model and report

Case study

Chess Data Platform

Magnus Carlsen’s public chess.com game history: 9,772 games landed raw, refined through silver and gold with PySpark, modeled as a star schema, served from Azure SQL and a Direct Lake model, and reported in Power BI.

  • Medallion lakehouse on Fabric, orchestrated with Data Factory
  • Model and report generated from code and deployed by API
  • Grain, dimensions, security and cost decisions documented
Read the case study
Chess Analytics dashboardInteractive analysis of any chess.com profile in the browser: ratings, openings, move trees, opponents, time of day.Open the dashboard
Chicago Sports LeaguesFive league operators scraped daily into one searchable page, with freshness tracking per source.Browse the leagues
Activate League Stats
Activate Global League StatsA season of a global league scraped, modeled and turned into a team review, awards and a full data explorer.Open the almanac

Tools

The Microsoft data platform first, plus what connects it to sources, version control and delivery.

  • Microsoft Fabriclakehouses, pipelines, Spark, Direct Lake
  • Azure Data Factoryorchestration, capacity bracketing
  • Azure SQL and SQL Server15+ years, T-SQL, tuning
  • Power BITMDL models, PBIR reports, DAX
  • Dimensional modelingKimball, conformed dimensions
  • PySpark and Delta Lakesilver and gold transforms
  • SSIS, SSRS, SSASthe estates being modernized
  • Pythondeploy scripts, data quality, generators
  • GitHubbranches, pull requests, squash merges
  • Azure and AWScloud foundations
  • Team leadershiparchitecture ownership, mentoring

Contact

R-Marq takes on data platform and BI engagements, from a scoped assessment to a full build, on contract or embedded in a client team. We are glad to walk through the case study live, with the capacity resumed and the pipeline running.

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Also on this site Games and tools built here as side projects, shared in case they are useful: the arcade and the tools.