Comparison
dbt transforms data that is already in your warehouse. It does not move it, so it is half a stack by design — and since the 2026 merger it sits inside Fivetran. Etlworks covers extraction, loading, CDC, and transformation in one platform.
The verdict
Feature breakdown
| Capability | Etlworks | dbt |
|---|---|---|
| Pricing & commercial | ||
| Starting price (monthly) | $300 | Free (dbt Core) / dbt Cloud from $100 per seat |
| Pricing model | Fixed per tier | Per developer seat |
| Cost transparency | High — flat rate | Medium — seat-based, enterprise negotiated |
| Vendor lock-in | Monthly or annual, no contract | dbt Core is open source; Cloud is annual |
| Ownership | Independent, founder-run | Fivetran + dbt Labs — merged June 2026 |
| Integration scope | ||
| Sources | 260+ | none — dbt transforms what is already loaded |
| Destinations | Warehouses, databases, SaaS, NoSQL, files, APIs, queues, IoT, email | The warehouse it runs against |
| ETL capabilities | ETL, ELT, Reverse ETL, wildcard processing | Partial — the T only — no extract, no load |
| API management | Full | |
| EDI processing | X12, EDIFACT, HL7, FHIR | |
| On-prem deployment | dbt Core self-hosted | |
| Embeddable | ||
| Transformations | ||
| Visual mapping | drag-and-drop designer with live preview | models are SQL |
| Scripting languages | SQL, JavaScript, Python, XSLT, shell | SQL and Jinja |
| Nested and hierarchical data | JSON, XML, Avro, Parquet — read, write, normalize, flatten by dragging | Partial — whatever the warehouse can unpack in SQL |
| Warehouse pushdown (ELT) | transform before load or in the warehouse, same engine | everything runs in the warehouse |
| Reusable logic | macros, templates, and 3,900+ prebuilt flow templates | macros, packages, and ref() |
| Lookups and enrichment | Lookup Builder for cross-source lookups | joins in SQL |
| Data validation | validation rules with per-step error handling | tests are a core feature |
| Orchestration & workflow | ||
| Scheduling | Cron expressions and fixed intervals, with per-schedule parameters | Cron jobs in dbt Cloud, or your own scheduler for dbt Core |
| Event-driven triggers | HTTP listeners and webhooks, message queues, file and email events | Partial — API-triggered job runs |
| Continuous execution | looping schedules for CDC and queue consumers | |
| Visual workflow builder | Composer canvas, 200+ flow types | models are SQL files |
| Nested workflows | nested flows with conditional and looped steps | model DAG built from ref() dependencies |
| Run external tools | shell and SSH scripts, CLI, JavaScript, Python, SQL, HTTP calls | |
| Parallel execution | overlapping schedules run as independent, separately cancellable instances | threads across the model DAG |
| Retries and error handling | per-step exception handling with notifications | Partial — job-level retry |
| Run monitoring | per-schedule status, run history, automatic Flow Findings reports | run history and model timing |
| CDC & Streaming | ||
| CDC engine | Debezium-compatible, built-in (no Kafka required) | |
| Database CDC sources | MySQL, Postgres, SQL Server, Oracle, MongoDB, DB2, others | |
| Streaming queues | Kafka, EventHubs, Kinesis, SQS, PubSub, ActiveMQ, RabbitMQ | |
| IoT brokers | MQTT brokers | |
| Real-time replication | Log-based CDC, full, incremental | batch models on a schedule |
| Change tracking modes | Log-based, trigger-based, timestamp/high-watermark | Incremental models, not change capture |
| Developer experience | ||
| REST API | full API for flows, connections, schedules, and runs | |
| CLI | full CLI with built-in SQL | the dbt CLI is the primary interface |
| MCP server | built-in — connect Cursor, Claude, or ChatGPT to your instance | |
| Client libraries | Python, Bash, and PowerShell clients | Python models on supported warehouses |
| Version control | built-in — automatic history, diff, and revert on every artifact | git, because models are files |
| Embeddable / white-label | ||
| Compliance & security | ||
| SOC 2 Type 2 | audited; report under NDA, SOC 3 public | |
| HIPAA | supported with a BAA | Partial — higher tiers only |
| GDPR / DPA | compliant, DPA available | |
| SSO and MFA | SAML SSO, optional 2FA, JWT stateless auth | Partial — enterprise tier |
| Role and artifact-level access | six roles plus tag-based scoping of flows, connections, and schedules | Partial — role-based, no artifact-level scoping |
| Encryption and data handling | TLS in transit, encrypted at rest, customer-managed PGP, SSH tunnels, IP allowlisting; rows are not persisted by default | encrypted in transit and at rest |
| Audit logging | admin actions logged, access logs monitored | Partial — enterprise tier |
| Security testing | monthly vulnerability and penetration scans, static analysis blocking every build, periodic third-party audits | vendor-managed, details on request |
| Gen AI | ||
| AI agent | Built-in agent (Simba) — builds and edits flows from chat | Partial — dbt Copilot in dbt Cloud — writes models and tests |
| Agent capabilities | Reads metadata, reads/samples data, writes JS & SQL, schedules, deploys, monitors | Model, test, and documentation generation |
| Natural-language flow building | ‘Vibe-build’ — create flows by describing what you want | Partial — Copilot drafts SQL models |
| AI-driven mapping | Auto-suggests source-to-destination mappings | |
| Built-in analytics | Agent runs analysis on flow data and pipeline behavior | |
| Chat across product | Same agent context on every screen | |
| CLI for agent | Full CLI access for run/deploy/monitor/manage | dbt CLI, not agent-driven |
| Trains on customer data | Never | Per dbt Labs terms |