Rich holden - Personal Portfolio

Technical Direction

In time, once complete, this section will become my [Technical Portfolio] page and be replaced with a section as I produce demonstrators of the kind of work I have delivered to employers, all the while being sensitive to employer IP and confidentiality.

For now, this serves as a statement of intent and recommendation for evolving Data Estates and Data Applications built on top of them, building Cloud Native Applications. This will be done by incrementally building a “Modern Data Platform” over a couple of years, working towards it in increments of 3-6 months at a time.

1 - Deliver Cloud Infrastructure Best Practice fit for building a Modern Data Estate

  • Engage your Infrastructure Teams: Assess AS-IS and propose a Workable Getting Started State.

  • Azure Tenancies: Configured and secured using Security Management Groups & Subscriptions.

  • Azure DevOps Services

    • Specify and Deploy Sandbox, Dev, Integration, Test, UAT, Pre-Prod and Prod Environments.

    • Readiness to manage-deliver Infrastructure-As-Code (IAC), Continuous Integration-Delivery (CI-CD).

    • Establish a Project and Programme Management approach to be lean and fit your organisation.

  • Development Tools

    • Git from the command line.

    • Visual Studio and Visual Code, Management Studio.

    • Co-Pilot for enhanced developer productivity.

  • Data Technology Set Up & Administration

    • AzureSQL and Administration.

    • Azure Data Factory.

    • Azure Synapse Analytics.

    • Azure Data Lake.

    • CosmosDb.

    • SQL Server Virtual Machines & Virtual Networks Hosting.

    • Managed Instance Set Up.

    • Data Bricks Instance Set up.

2 - Build Data Solutions to Academic Principles in a Business Settings: Develop & integrate teams

  • Enterprise Data Architecture with Design in Data Architecture Tools when appropriate.

  • Provide ‘Data Architect Services’ to understand the true ‘Data Estate’: Complexity, Volumes, Use Cases.

  • For each Data Technology look to:

    • Identify a Business Focus Area.

    • Sufficiently small to be practical, large enough to demonstrate concept & value of Data Technology.

    • Understand Business Data and apply base technology to that business data.

    • Identify appropriate Microsoft Product to build out Data Technology to Microsoft Best Practice.

  • Ensuring usable clear functionality is delivered quickly, rather than unusable product experimentations.

  • Ensure Target Platforms deliver cost-effective Data Solutions adding business value, not an Azure Data Mess.

  • Focus on data product delivery within well-managed teams, w/work & learning well-distributed across teams.

  • Feeding into an emerging Data Strategy incorporating appropriate Data Methodologies being investigated.

  • Learn about the complexity of your data estate and how to sort it out.

3 - Data Warehouse Delivery: ‘On-Prem Proof of Concept’ to ‘Modern DWH in Microsoft Azure Cloud’

  • Build Data Warehouses to Best Practice Methodologies and Academic Theory.

  • Deliver quick win: Simple On-Prem Data Warehouse - Minimal Complexity, Real Business Data.

  • Port to various Azure Technologies, to align Infrastructure, Cyber-Ops, Development and Data Teams.

    • On-Prem DWH (SQL Db Diagrams, Data Models & Monitored Data Loads using SSIS).

    • Port Solution to AzureSQL / Azure Data Factory (ADF) w/SSIS Packages on Integration Runtimes.

    • Port Solution to Azure Synapse Analytics / Azure Data Factory (ADF) w/out SSIS Packages & IRs.

    • Port Solution to a CosmosDb.

    • Port Solution to SQL Server Virtual Machines & Virtual Networks Hosting (work w/Infrastructure)

    • Port Solution to a Managed Instance Infrastructure (working w/Infrastructure).

  • In parallel run Azure and Data Training Sessions for your teams, as appropriate.

  • Choose Final Target Architecture for the Modern Azure Data Warehouse, as suitable for your business

    • Work with Infrastructure/DevOps/Cybersecurity to build it right.

    • Scope out and set up a Programme of Work to build out an Enterprise Data Warehouse.

    • Help solicit Sponsorship, Budget and Resource the various Data Projects.

    • Identify blockers and solutions to remove them, recruit or supply resource as necessary.

4 - Data Arch Lead: Warehouses, Lakehouses, Fabrics, Meshes, Big Data, Streaming, Catalogs, Governance, AI

  • Lead research, on behalf of employers, into emerging newer data methodologies.

    • Sharing knowledge on how to investigate, and stay on top of emerging approaches and tech.

    • Researching theory, building base prototypes in base technologies independent of Product Vendors.

    • Allows illustration of base concepts concretely and to train your teams, sharing as-we-learn.

    • Work w/Microsoft-Infrastructure on Microsoft Product Offerings: getting installed to best practice.

    • Port the new prototypes into the installed Products.

    • Work w/Microsoft to understand Product Efficiencies & how to work-around Product Deficiencies.

    • Identify Business Data that will allow concepts to be applied against real business data to add value.

    • Provide Data Architecture Services: Design & lead the build of real solutions in all these areas.

    • Work w/Data Office: Assure security models in place & compliance w/all data, IT & business standards.

  • Examples of Emerging Methodologies keen to build out and learn-improve knowledge on include:

    • Developing a Searchable Enterprise Data Catalogue (Prototyping on a Graph Database).

    • Investigate Microsoft Purview, Clued-In, Profisee: develop Enterprise Data Catalogue using products.

    • Learn about and implement a Data Fabric.

    • Learn about and implement a Data Mesh.

    • Learn about and implement AI Applications: Data Science, ML, Big Data Technologies.

  • Hone a costed, cloud-based Data Strategy for the business, targeting Microsoft Azure or even multi-cloud.