Modern Data Platform
Reference architecture for cloud-native data platforms. Stack choices adapt to your scale, existing tools, and compliance requirements.
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Decision Log
Architecture Decisions
- Warehouse vs. lakehouse, Pure warehouse for BI; lakehouse adds unstructured/ML workloads at complexity cost.
- Batch vs. streaming, Start batch-first; add streaming only where latency requirements justify complexity.
- Transformation layer, dbt for SQL-first teams; Spark for complex ML feature engineering.
- Cost model, Separate compute from storage; set resource monitors and auto-suspend.
Integration & Security
- Source connectors, Fivetran/Airbyte for SaaS; custom for legacy or high-volume CDC.
- Access control, RBAC with row-level security for multi-tenant or sensitive data.
- Data quality, dbt tests + Great Expectations for contract validation.
- Catalog & lineage, Atlan, DataHub, or native catalog for discoverability and impact analysis.
Build a clean, governed data foundation that unlocks analytics today and AI tomorrow. We right-size for your stage - no Fortune 500 bloat, no spreadsheet sprawl.
Cost & Reliability Levers
Cost Levers
Warehouse sizing
Right-size compute for workload; auto-suspend during idle periods.
Avoid: Running XL warehouses 24/7 for occasional queries.
Impact: 40-60% compute savings
Query optimization
Optimize hot queries with clustering, pruning, and caching.
Avoid: Full table scans on every dashboard refresh.
Impact: 2-10x faster queries
Data tiering
Move cold data to cheaper storage; archive instead of delete.
Avoid: Keeping 5 years of transactional data in hot storage.
Impact: 30-50% storage savings
Materialization strategy
Pre-aggregate for dashboards; incremental for large tables.
Avoid: Full refresh of billion-row tables every hour.
Impact: 50-80% less compute
Reliability Levers
Data contracts
Define schema + freshness SLAs between producers and consumers.
Avoid: Silently breaking downstream dashboards with schema changes.
Impact: Zero surprise breakages
Automated testing
dbt tests + Great Expectations on every pipeline run.
Avoid: Manual spot-checking after incidents.
Impact: Catch 95% of issues before users
Monitoring & alerting
Row counts, freshness, and cost anomalies with Slack/PagerDuty.
Avoid: Finding out from the CFO that the board deck is wrong.
Impact: MTTR under 30 minutes
Rollback capability
Time travel + blue-green deployments for model changes.
Avoid: No way to recover from bad transformations.
Impact: Recovery in minutes, not hours
What You Leave With
Architecture diagram
Production-ready design with stack choices, data flows, and security boundaries.
Data contracts
Schema definitions, SLAs, and ownership for each data product.
dbt project
Modeled datasets with tests, documentation, and CI/CD pipeline.
Access model
RBAC policies, row-level security rules, and audit logging.
Cost playbook
Query tagging, warehouse sizing, and optimization runbook.
Runbooks
Incident response, backfill procedures, and on-call documentation.
What you'll have
Single source of truth
Consistent metrics and governed data models.
Faster answers
Self-service analytics on curated, documented datasets.
Cost control
Right-sized warehouses and pipelines tuned for spend.
AI readiness
Structured, secure, high-quality data ready for LLMs/ML.
Technical scope
Questions we hear
Do you replace our BI tool?
We work with your stack - Looker, Power BI, Tableau, Mode, or Metabase.
Can you reduce our Snowflake/warehouse bill?
Yes. We tune warehouses, caching, pruning, and model design for cost.
How long to first value?
Most teams see modeled, trusted datasets in 4-6 weeks.