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 & testsschema/compatibility, nulls/ranges/PK/FK, metric parity.
- Modelingconformed dimensions, facts, SCD1/SCD2 history, semantic layer definitions (revenue, churn, SLAs).
- Performanceclustering/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
🔄 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
Key terms in plain language
Open a term for a concise explanation of language used on this page.
Latency
The time it takes data to travel between two points. Lower latency improves voice, video meetings, cloud applications, gaming, and other real-time services.
Software as a Service (SaaS)
Software accessed as an online service instead of being installed and maintained entirely on the customer’s own computers or servers.
Cybersecurity
The practices and controls used to protect identities, devices, networks, applications, and data from unauthorized access, disruption, or manipulation.
Identity and Access Management (IAM)
The systems and policies that determine who a user is, what resources they may access, and how that access is authenticated and reviewed.
Multi-Factor Authentication (MFA)
A login control requiring more than one form of verification, such as a password plus an authenticator app, security key, or biometric factor.
API
An application programming interface is a defined way for software systems to exchange data or request functions from one another.
Artificial Intelligence (AI)
Software designed to perform tasks involving prediction, classification, generation, reasoning, or decision support. Business use still requires clear data, governance, security, and human accountability.