Data platform architecture
Designing clearer platform foundations across lakehouse, warehouse, semantic, and operational layers.
Senior Data Engineer
Data Platform Craftsman
Passionate in Data Architecture
Azure · AWS · GCP
Databricks · Fabric
Rujikorn Ngoensaard
XH / bossruji · xhverse.co
XHVERSE is my data engineering journal and portfolio — covering platform design, governance patterns, and analytics delivery across Azure, Databricks, and Microsoft Fabric.
A data platform craftsman: practical, production-focused, and built for teams that ship.
Work focus
Clearer data ecosystems for real enterprise teams.
The work is technical, but the goal is operational clarity: platforms that teams can govern, extend, monitor, and explain with confidence.
Governance and standardization
Turning fragmented access, naming, ownership, and operating models into maintainable platform rules.
Reporting & analytics enablement
Migrating legacy BI to modern platforms, building semantic layers, and enabling self-service reporting at enterprise scale.
Architecture communication
Translating technical platform decisions into practical narratives for delivery teams and stakeholders.
Capability system
Platform, governance, and delivery — end to end.
Platform
Azure Databricks Fabric AWS
Architecture
Lakehouse Medallion Workspace design
Data Layer
Delta Lake ADLS Gen2 Parquet
Engineering
Spark ADF Airflow Python SQL
Analytics & Gov
Power BI Tableau Unity Catalog
Platform architecture
Lakehouse foundations Workspace models Data product paths
Governance systems
Access design Ownership patterns Audit-ready standards
Analytics delivery
Semantic layers Reporting foundations Operational handover
Tools
Practical checks for data platform clarity.
Free tools and labs for platform decisions, access design, table layout, semantic models, recovery planning, and fast architecture checks. No login required.
Egress calculator
Cloud Egress Pricing Calculator
Estimate AWS, Google Cloud, and Azure egress costs for Thailand and Singapore internet, external cloud, and same-cloud regional paths.
Open tool →
Placement simulator
Workload Placement Simulator
Compare Databricks, Fabric, and Power BI placement paths for a workload. Get a directional recommendation, runners-up, and review warnings.
Open tool →
Contract builder
Data Product Contract Builder
Turn ownership, grain, freshness, quality, access, lifecycle, and consumer expectations into a copyable data product contract brief.
Open tool →
Access simulator
Access Model Simulator
Pressure-test Databricks, Fabric, and Power BI access paths against least-privilege controls, sensitivity, and masking expectations.
Open tool →
Layout advisor
Lakehouse Table Layout Advisor
Assess table size, file pressure, query filters, update patterns, and engine mix to get layout and maintenance guidance.
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Model doctor
Power BI Semantic Model Doctor
Check fact grain, relationships, measures, refresh mode, and report symptoms before a semantic model becomes slow or inconsistent.
Open tool →
Selected work themes
Practical patterns for complex platform environments.
A focused view of the platform problems, engineering decisions, and operating outcomes that shape the work.
01 Azure · Databricks · Unity Catalog
Lakehouse governance realignment
Clarifying workspace access, catalog permissions, policy boundaries, and operating rules so teams can use the platform without growing permission sprawl.
Visible outcomes
- Cleaner access paths
- Stronger ownership model
- Audit-friendly documentation
02 Microsoft Fabric · Power BI · Automation
Fabric operating model design
Turning workspace administration, semantic model movement, and role assignment flows into repeatable practices that are easier to verify and maintain.
Visible outcomes
- Safer admin workflows
- Clearer release checks
- Reusable operator runbooks
03 Data engineering · Architecture narratives · Governance
Enterprise analytics foundation
Connecting delivery teams, stakeholders, and platform owners through practical architecture decisions, clearer standards, and decision-ready communication.
Visible outcomes
- Shared platform language
- Reduced ambiguity
- Better long-term maintainability
Public proof
Writing, code, and professional context — all public.
Operating principles
Architecture should make delivery easier to run.
01
Clear contracts before clever systems
02
Governance that operators can actually run
03
Architecture decisions tied to business context
04
Platforms designed for maintenance, not only launch
Writing
Latest notes on platforms, governance, and analytics delivery.
· 15 min read
CDC is a production contract, not an ingestion shortcut
Change Data Capture matters because it turns source-system change into ordered, replayable data contracts, but only when teams design for snapshots, retention, schema drift, deletes, and recovery.
data-engineering cdc lakehouse
Read article → · 9 min
Data Architecture Is the Blueprint, Not the Pattern
Data architecture is not a stack label, data model, or implementation pattern; it is the operating blueprint that connects ownership, governance, serving contracts, cost, latency, reliability, and change.
data-architecture platform governance
Read article → · 7 min read
PySpark data quality checks that belong in production pipelines
A practical way to group PySpark data quality checks by the production failures they prevent: broken contracts, duplicate loads, stale data, invalid business logic, and silent drift.
spark data-quality data-engineering
Read article → Contact
Architecture, governance, and platform advisory.
Open to architecture consulting, platform advisory, and data governance conversations.
