Barny Self

Barny Self

Senior Microsoft Data Platform Consultant for Coeo.

Reading, United Kingdom

With over 10 years specialising in both the Microsoft SQL Server BI stack and more recently the Azure Data Platform, Barny has designed and implemented both internal and customer facing BI platforms, and has a specialisation in performance optimisation against large data sets in both SQL Server and the Tabular engine powering both Analysis Services and Power BI.

More recently, Barny has been digging deep into finding common ETL patterns and implementing metadata driven Databricks pipelines to aid in onboarding new feeds into the delta lake in an efficient manner using tested code.

Area of Expertise

  • Information & Communications Technology


  • Azure
  • Cloud Computing on the Azure Platform
  • Microsoft Azure
  • Azure Data Platform
  • Azure Functions
  • Azure SQL Server
  • Azure SQL Database
  • Azure Analysis Services
  • Databricks
  • Azure Databricks
  • • Python

Unity Catalogue and Purview: Data Governance Bedfellows

With the strict legal requirements covering the storage and usage of data, Data Governance requirements have come to the forefront for many organisations.

Whilst there are many governance solutions out there, each solution has its own pros and cons. Consequently, organisations may need to use multiple solutions to have the broadest coverage of data within the organisation.

In this session, you will see how we can tie two specific solutions together, Databricks Unity Catalog and Microsoft Purview, to automatically monitor the data lineage in Unity Catalog and then present this information in the broader Governance solution that is Purview.

Data as Code: CI/CD for your metadata

With the rise of metadata driven development, storing and version controlling the metadata stored in the configuration database presents a significant challenge.

In this session, two Consultants will present the Azure DevOps CI/CD pipelines that allow you to promote your metadata changes from dev through to test and finally prod via source control.

Are you accelerating?

A demonstration of how to use the metadata of data feeds to drive metadata driven ETL processes in Azure Databricks to enable swift ingestion of new feeds of data from raw all the way though conformed and then onto a SQL Server datawarehouse.

Mapping Data Flows, multiformat fiat file processing

In this session we will learn how to process the most complex of file formats, the multiformat flat file

Performance optimisation of large data sets using aggregations

In this session, Barny will take you through optimising reporting from large data sets in Power BI by learning how to use aggregations and how to select the correct aggregation levels.

Barny Self

Senior Microsoft Data Platform Consultant for Coeo.

Reading, United Kingdom

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