Extract–Load–Transform in the Warehouse/Lake (Fast, Governed, AI-Ready)
ELT loads raw data first into your warehouse/lake, then transforms in-place using its native compute.
SolveForce builds ELT so modeling is fast, governed, and cost-aware—with data contracts, tests, lineage, and SLO dashboards. The result: trusted tables and features for BI/ops/AI, with evidence for audits.
See both patterns → ETL / ELT
Targets & serving → Data Warehouse / Lakes • Governance → Data Governance / Lineage
🎯 Outcomes (Why ELT)
- Speed & scale — push heavy transforms to MPP/lakehouse engines (cheaper, elastic).
- Governed modeling — data contracts, tests, lineage, and approvals inside the platform.
- Low latency to value — land raw quickly; iterate models without re-extracting.
- AI-ready — curated tables → embeddings/feature stores with provenance. → Vector Databases & RAG • AI Knowledge Standardization
- Cost control — partitioning/pruning/caching; auto-suspend/scale warehouses.
🧭 Scope (What we ingest & transform)
- Sources: OLTP DBs (Oracle/SQL Server/Postgres/MySQL), ERP/CRM/HR, SaaS APIs, logs/metrics, IoT/OT streams, files (CSV/JSON/Parquet), CDC streams.
- Destinations: curated zones, conformed dims/facts, semantic marts, feature stores, and vector indices in your warehouse/lake. → Data Warehouse / Lakes
🧱 ELT Building Blocks (Spelled Out)
- Load — land raw/staging with provenance (source, version, extract time, checksum).
- Transform in-place
- Contracts & tests: schema/compatibility, nulls/ranges/PK/FK, metric parity.
- Modeling: conformed dimensions, facts, SCD1/SCD2 history, semantic layer definitions (revenue, churn, SLAs).
- Performance: clustering/partitioning, materialized views, pruning & result cache.
- Orchestration — DAGs, retries/backoff, backfills, event triggers; PR-based changes.
- Docs & lineage — column-level lineage, impact analysis, glossary labels in catalog. → Data Governance / Lineage
Privacy note: If policy requires redaction/tokenization before storage, use ETL first, then ELT for modeling.
🏗️ Reference Architecture (Land → Validate → Model → Serve → AI)
1) Land (Raw/Staging)
- Batch/CDC/stream to tables/files; attach provenance & loads metrics.
2) Validate - Contracts & tests (dbt/SQL, Great Expectations); tag PII/PHI/PAN.
3) Model (ELT) - Build core (dims/facts), marts, and semantic layer; SCD2 where history matters.
4) Secure & Govern - IAM roles, row/column security, masking/tokenization; keys in KMS/HSM; logs to SIEM. → IAM / SSO / MFA • Encryption • Key Management / HSM • SIEM / SOAR
5) Serve - BI/SQL/APIs; materializations & caching; workload isolation.
6) Publish to AI - Curated & labeled sets → feature stores/vector indices with citations. → Vector Databases & RAG
🔄 ELT vs ETL (Decision Guide)
| Situation | Choose |
|---|---|
| Need heavy joins/aggregations at scale, low latency modeling | ELT |
| Governance & lineage live in the warehouse/lake | ELT |
| Must mask/tokenize PII before landing | ETL (then ELT) |
| Specialized transforms before storage, strict edge latency | ETL |
| Hybrid privacy-then-modeling | ETL ➜ ELT |
See both patterns → ETL / ELT
🔐 Security, Privacy & Keys
- PII labels at land; mask/tokenize at transform when policy allows in-platform. → DLP
- Encryption in transit & at rest; CMK/HSM custody; vault for secrets/creds. → Encryption • Key Management / HSM • Secrets Management
- Least-privilege IAM for pipelines; short-lived credentials; approvals for destructive ops. → IAM / SSO / MFA
- Evidence: tests, changes, and job logs stream to SIEM/SOAR. → SIEM / SOAR
📐 SLO Guardrails (Experience & Reliability You Can Measure)
| SLO / KPI | Target (Recommended) | Notes |
|---|---|---|
| Freshness (core marts) | ≤ 15–60 min | CDC/streaming for hot tables |
| Query latency (p95) | BI: ≤ 1–3 s • Ad-hoc: ≤ 5–10 s | With pruning/clustering |
| Data quality pass rate | ≥ 99% tests green | Contract + test gates |
| Schema drift detect → ticket | ≤ 5 min | Auto PR for fixes |
| Job success (rolling 30d) | ≥ 99% | Retries/backoff |
| Cost / TB scanned (p95) | Budgeted thresholds per domain | Partitioning/caching |
| Lineage coverage (curated) | ≥ 95% | Column-level where possible |
SLO breaches open tickets and trigger SOAR (retry/backfill/escalate). → SIEM / SOAR
🧰 Patterns (By Outcome)
A) Lakehouse ELT
- Land Parquet + Iceberg/Delta tables → dbt SQL models → marts & semantic layer → BI + vector export.
B) SaaS Consolidation (Finance/CS)
- API loads → staging → contracts/tests → conform dims/facts (account/customer/case) → marts; masking for Restricted columns.
C) Real-Time Features
- Stream to hot tables; ELT windows join/enrich; publish to feature store with provenance.
D) Regulated Analytics
- Row/column security (RLS/CLS), masking, CMK/HSM keys; immutable logs; evidence packs for audits.
🔎 Observability & FinOps
- Dashboards — freshness, success %, TB scanned, slot/warehouse time, queue lag.
- Tracing — job spans, slow transforms, retries.
- Budgets & alerts — per domain; auto-tune clustering/materializations; cost comments in PRs.
- Drift watch — permission drift & data drift; alert to NOC/SecOps. → NOC Services
📜 Compliance Mapping (Examples)
- PCI DSS — encryption, masking, logging; evidence of access and change.
- HIPAA — PHI labeling, minimum necessary, audit trails.
- ISO 27001 — ops security, access mgmt, change evidence.
- NIST 800-53/171 — AU/AC/SC/CM families; crypto, audit, config.
- CMMC — CUI labeling & retention controls.
All artifacts stream to SIEM; runbooks in SOAR for rollback & incident response.
🛠️ Implementation Blueprint (No-Surprise Rollout)
1) Inventory & SLAs — sources, freshness, privacy constraints.
2) Contracts & catalog — registry + compatibility; glossary alignment. → Data Governance / Lineage
3) Pipelines — batch + CDC + streaming; idempotent merges; backfills.
4) Modeling — dbt/SQL; conformed dims/facts; semantic metrics as code.
5) Security — IAM least privilege; masking/tokenization; CMK/HSM; secrets from vault.
6) Lineage & docs — auto-capture; PR-based changes with reviewer gates.
7) SLOs & dashboards — freshness/latency/DQ/cost; alerts to NOC/SecOps. → SIEM / SOAR • NOC Services
8) AI publish — curated outputs → vector/feature stores with labels & provenance. → Vector Databases & RAG
9) Drills — schema-break, backfill, scale-out; publish RCAs & improvements.
✅ Pre-Engagement Checklist
- 📚 Source list & SLAs; CDC/stream feasibility; API rate limits.
- 🔐 PII/PHI/PAN plan (mask/tokenize/encrypt); IAM roles & secrets.
- 🗂️ Contracts, catalog, lineage platform.
- ☁️ Compute/storage tiers; partitioning/clustering; cost alarms.
- 📊 SIEM/SOAR integration; alert & approval matrix.
- 🧪 Pilot domain; backfill strategy; rollback plan.
🔄 Where ELT Fits (Recursive View)
1) Grammar — data rides Connectivity & Networks & Data Centers.
2) Syntax — compute/storage in Cloud; curated targets in Data Warehouse / Lakes.
3) Semantics — Cybersecurity