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On-prem installers
Sign in| Feature | Etlworks | Apache Airflow |
|---|---|---|
| Category | Low-code + code-first hybrid integration and orchestration platform | Open-source Python-based workflow orchestrator |
| Primary Audience | Data teams wanting full integration + orchestration without heavy DevOps | Engineering teams preferring Python-defined workflows |
| Focus | ETL, ELT, CDC, orchestration, API integration, transformations, automation | Pipeline orchestration, scheduling, DAG execution |
| Skill Requirement | Low to intermediate (visual UI + optional scripting) | High (Python, DevOps, Docker/Kubernetes) |
| Setup & Maintenance | No-code setup, fully managed cloud or on-prem, minimal DevOps | High DevOps effort: cluster management, schedulers, workers, logs |
| ETL & Transformations | Built-in mapping, SQL/JS/Python, lookups, enrichment, normalizing, CDC flows | No native ETL engine. Requires writing custom Python tasks or external tools |
| API Integration |  |
Requires custom Python operators or plugins |
| Data Replication / CDC | Log-based CDC, Full, Incremental, real-time agents | No CDC. Must integrate 3rd-party tools |
| Code-First Support | Full scripting: SQL, JavaScript, Python, Shell; run locally or remotely | Python only |
| CLI Experience | Full integrated CLI: commands, scripts, data queries, remote execution | Basic CLI for DAG management |
| Automation Capabilities | Events, triggers, schedules, webhooks, queues, file watchers, agents | Primarily cron-like scheduling + DAG dependencies |
| Error Handling & Recovery | Automatic retries, reruns, partial loads, error pipelines | Retries + email alerts only; custom coding for advanced recovery |
| Scalability | Horizontal and vertical scaling, multi-node, HA | Scales well but requires Kubernetes or Celery setup |
| Observability | Built-in monitoring, logs, data previews, metrics, lineage-lite | Requires additional tools (Grafana, Prometheus, ELK) |
| Deployment | SaaS, on-prem, hybrid-cloud agents | Self-managed; requires container orchestration |
| Total Cost of Ownership | Low; no infrastructure or development burden | High; Python development + cluster maintenance |
| Pricing | $300–$4500+ monthly | Free open source; high ops cost for production usage |
Etlworks vs. Airflow
Compare a modern low-code data integration platform with the leading code-first orchestration framework.
Side-by-side Comparison
Difference
Why Teams Choose Etlworks Over Airflow
End-to-End Integration, Not Just Orchestration
Airflow orchestrates tasks but does not handle ETL, CDC, transformations, or API integration natively. Etlworks includes all of this out of the box, reducing tooling sprawl.
Full Code-First and Low-Code Flexibility
Etlworks supports SQL, JavaScript, Python, and Shell. Code can run inside the platform or remotely using agents. Airflow requires Python for everything.
Integrated CLI and Automation Engine
The new Etlworks CLI executes commands, pipelines, scripts, and transformations locally or across nodes. Airflow offers a basic DAG management CLI only.
No DevOps Required
Airflow requires ongoing maintenance of schedulers, workers, queues, logs, and databases. Etlworks runs fully managed or lightweight on-prem without cluster complexity.
Upgrade to a Modern Orchestration + Integration Platform
Airflow is powerful, but requires Python expertise and constant DevOps effort. Etlworks provides the same orchestration flexibility with a complete ETL, CDC, API integration, and automation stack built in.