Clean, Governed, AI-Ready Analytics
A Data Warehouse / Lake is your single source of truthful analytics—fast queries, fresh data, and governed access.
SolveForce designs modern warehouses (or lakehouses) that ingest, validate, model, secure, and serve data for BI, operations, and AI. You get repeatable pipelines, clear lineage, low-latency queries, and evidence for audits.
Where this fits in the SolveForce model:
☁️ Platform → Cloud • 🔄 Pipelines → ETL / ELT
📚 Catalog & Policy → Data Governance / Lineage • 🧩 MDM → Master Data Management
🤖 AI & RAG → Vector Databases & RAG • AI Knowledge Standardization
🔒 Security → Cybersecurity • DLP • Encryption • Key Management / HSM
🎯 Outcomes (What a good warehouse delivers)
- One version of truth — conformed dimensions, reconciled facts, and business rules as code.
- Fast & fresh — sub-second to seconds query latency, minute-to-hour freshness SLAs.
- Governed & compliant — row/column security, masking, lineage, and audit-grade logs.
- AI-ready — curated tables → embeddings → guarded RAG with citations.
- Cost control — predictable spend (slot/warehouse sizing, caching, pruning, auto-suspend).
🧱 Building Blocks (Spelled out)
- Storage — columnar (Parquet/ORC), Iceberg/Delta/Hudi tables for ACID in lakes.
- Compute — MPP engines (Snowflake / BigQuery / Redshift / Synapse / Databricks SQL Warehouse).
- Ingest — connectors, change data capture (CDC), streaming (Kafka/Kinesis/Pub/Sub). → ETL / ELT
- Modeling — star/snowflake schemas, data vault where helpful, semantic layer.
- Orchestration — DAGs, Airflow/DBT/Workflows; retries, SLAs, backfills.
- Catalog — business glossary, schema registry, lineage graph, PII tags. → Data Governance / Lineage
- Security — IAM roles, RLS/CLS, tokenization/masking, KMS/HSM-backed keys. → Encryption • Key Management / HSM
- Serving — BI (Looker/Power BI/Tableau), APIs, feature stores, vector indexes. → Vector Databases & RAG
🏗️ Reference Architecture (Ingest → Validate → Model → Serve)
1) Ingest
- Batch (files/DB dumps), CDC (Debezium/Datastream/DMS), Streaming (Kafka/Kinesis).
- Land to staging with raw schema + provenance. → ETL / ELT
2) Validate & Profile
- Data contracts; schema checks; tests (nulls, ranges, uniqueness, referential).
- PII detection + tags for governance/DLP. → Data Governance / Lineage • DLP
3) Transform & Model
- ELT in-warehouse (DBT/SQL); build core marts (dimensions/facts) and semantic models.
- Versioned SQL + CI (unit tests on queries).
4) Secure & Govern
- IAM, RLS/CLS, dynamic masking; KMS/HSM keys; audit logs to SIEM.
- Row policies by jurisdiction for data residency. → Cybersecurity • SIEM / SOAR
5) Serve & Optimize
- BI, ad-hoc SQL, APIs; materializations & caching; auto-suspend/scale warehouses.
- Publish curated datasets to vector indexes for AI retrieval. → Vector Databases & RAG
🔒 Security & Privacy (Zero-Trust Data)
- Access-first — ABAC/RBAC via IAM groups & tags; least-privilege grants. → IAM / SSO / MFA
- Row/Column controls — RLS (tenant/region), CLS masking (e.g., hash, null, partial).
- PII/PHI/PAN handling — label + tokenize or encrypt at field level; deny ungoverned exports. → DLP • Encryption
- Key custody — CMK/“Hold Your Own Key” patterns with HSM-backed KEKs. → Key Management / HSM
- Audit — query logs, grants, data movements to SIEM/SOAR for incident & compliance. → SIEM / SOAR
📐 SLO Guardrails (Experience you can measure)
| SLO / KPI | Target (Recommended) | Notes |
|---|---|---|
| Freshness (core marts) | ≤ 15–60 min | CDC/streaming pipelines for hot tables |
| Query latency (p95) | BI: ≤ 1–3 s • Ad-hoc: ≤ 5–10 s | With clustering & pruning |
| Data quality pass rate | ≥ 99% tests green per run | Contracts + CI checks |
| Lineage coverage | ≥ 95% of curated tables | Auto-captured + manual links |
| Cost / TB scanned | Budget & alert thresholds per domain | Partitioning, caching, Z-ordering |
| Access error rate | ≤ 1% (mis-grants) | Continuous permission tests |
Dashboards live with BI and SIEM/SOAR; alerts for freshness, cost spikes, failed tests, and access drift.
🧭 Modeling Principles (Keep it understandable)
- Conformed dimensions (Customer/Product/Time/Geo) shared across marts.
- Clear grain for each fact (e.g., order line, session event).
- Semantic layer for business metrics (revenue, churn, ARR, SLA attainment) to avoid ad-hoc divergence.
- Slowly Changing Dimensions (SCD2) for history; SCD1 where only latest matters.
💡 Performance Patterns (Fast without overpaying)
- Cluster & prune on date/tenant/region; partition large tables.
- Materialize common joins/aggregates; auto-vacuum/optimize lake tables.
- Result caching; query acceleration services where sensible.
- Workload isolation — dedicated warehouses/slots per team or SLA.
🧩 AI & RAG Integration (Grounded, cited answers)
- Publish curated tables as the ground truth to embedding pipelines.
- Build domain-sharded vector indexes with labels (product, policy, region). → Vector Databases & RAG
- Enforce filter-first retrieval, rerank with ontology signals, cite sources, or refuse. → AI Knowledge Standardization
🧪 Data Quality & Contracts (Fail fast, fix early)
- Tests at staging (schema), at transform (logic), and at serve (metric parity).
- Contracts with producers (fields/types/SLA); break builds on incompatible changes.
- Drift watch — alert on null-surges, distribution shifts, duplicate keys.
📜 Compliance Mapping (Examples)
- PCI DSS — PAN tokenization/masking; access logs; encryption at rest/in transit.
- HIPAA — PHI labeling, minimum necessary, audit trails.
- ISO 27001 — ops controls, access management, evidence.
- NIST 800-53/171 — AC/AU/CM/SC families for access, audit, change, crypto.
- CMMC — CUI labeling, RBAC, retention.
Evidence streams to SIEM/SOAR; DLP prevents unsafe channels; encryption keys under CMK/HSM.
🛠️ Implementation Blueprint (No-surprise rollout)
- Inventory domains & KPIs — pick highest-value marts (finance, product, support, security).
- Landing & staging — set contracts and PII tags; automate profiling.
- Model & semantic layer — conformed dims, fact grains, metric definitions as code.
- Govern — IAM roles, RLS/CLS, masking policies, lineage, approvals. → Data Governance / Lineage
- Serve — BI models, APIs, extracts; cache/materialize; isolate workloads.
- AI publish — export curated sets to vector pipelines with labels & provenance. → Vector Databases & RAG
- Observe — freshness, cost/TB, test pass rate, access drift; alert to NOC/SecOps. → NOC Services • SIEM / SOAR
- Harden & audit — DLP, tokenization, CMK/HSM keys, retention & legal hold. → DLP • Key Management / HSM
✅ Pre-Engagement Checklist
- 📦 Source systems, CDC feasibility, streaming needs.
- 🧭 Target marts & semantic metrics; BI tools and SLAs.
- 🔐 PII/PHI/PAN classes, tokenization/encryption strategy. → DLP • Encryption
- 👥 IAM roles, RLS/CLS policies, jurisdictions. → IAM / SSO / MFA
- 📚 Catalog/lineage platform and labels. → Data Governance / Lineage
- 🔄 Orchestration & CI/CD for SQL/DBT; test suite coverage. → ETL / ELT
- 💰 Budget: slots/warehouses, storage tiers, cost alarms.
🔄 Where Data Warehouse / Lakes Fit (Recursive View)
1) Grammar — data travels on Connectivity & Networks & Data Centers.
2) Syntax — Cloud hosts storage & compute patterns (lakehouse, MPP).
3) Semantics — Cybersecurity + DLP preserve truth & privacy.
4) Pragmatics — SolveForce AI retrieves from curated truth with citations.
5) Foundation — Primacy of Language and ontology keep terms coherent.
6) Map — indexed across the SolveForce Codex & Knowledge Hub.
📞 Build a Warehouse That’s Fast, Governed & AI-Ready
Related pages:
ETL / ELT • Data Governance / Lineage • Master Data Management • Vector Databases & RAG • AI Knowledge Standardization • Cloud • Cybersecurity • Key Management / HSM • Encryption • DLP • Knowledge Hub