Comparison
Azure Data Factory is the obvious pick if you're all-in on Azure. Etlworks gives you the same integration capabilities across multi-cloud, on-prem, and hybrid — with predictable pricing and a visual designer that doesn't lean on JSON pipeline definitions.
The verdict
Feature breakdown
| Capability | Etlworks | Azure Data Factory |
|---|---|---|
| Pricing & commercial | ||
| Starting price (monthly) | $300 | Per-activity + DIU-hours |
| Pricing model | Fixed per tier | Consumption (activities + DIU-hours) |
| Integration scope | ||
| Sources | 260+ | 90+ (Azure-centric) |
| ETL capabilities | ETL, ELT, Reverse ETL, wildcard processing | ETL/ELT |
| API management | Full | |
| On-prem deployment | Partial — Self-hosted IR | |
| Transformations | ||
| Visual mapping | drag-and-drop designer with live preview | Mapping Data Flows |
| Scripting languages | SQL, JavaScript, Python, XSLT, shell | Data Flow expressions, SQL, notebooks |
| Nested and hierarchical data | JSON, XML, Avro, Parquet — read, write, normalize, flatten by dragging | Partial — passes JSON through, unpacking happens downstream |
| Warehouse pushdown (ELT) | transform before load or in the warehouse, same engine | |
| Reusable logic | macros, templates, and 3,900+ prebuilt flow templates | pipeline templates and datasets |
| Lookups and enrichment | Lookup Builder for cross-source lookups | |
| Data validation | validation rules with per-step error handling | |
| Orchestration & workflow | ||
| Scheduling | Cron expressions and fixed intervals, with per-schedule parameters | Schedule, tumbling window, and event triggers |
| Event-driven triggers | HTTP listeners and webhooks, message queues, file and email events | blob events and custom Event Grid topics |
| Continuous execution | looping schedules for CDC and queue consumers | Partial — tumbling windows and Mapping Data Flow streaming |
| Visual workflow builder | Composer canvas, 200+ flow types | pipeline canvas |
| Nested workflows | nested flows with conditional and looped steps | Execute Pipeline, ForEach, If, Until |
| Run external tools | shell and SSH scripts, CLI, JavaScript, Python, SQL, HTTP calls | Partial — Web, Azure Function, Batch, and Databricks activities |
| Parallel execution | overlapping schedules run as independent, separately cancellable instances | parallel activities and ForEach batching |
| Retries and error handling | per-step exception handling with notifications | activity retry policies and failure paths |
| Run monitoring | per-schedule status, run history, automatic Flow Findings reports | pipeline run history and alerts |
| CDC & Streaming | ||
| CDC engine | Debezium-compatible, built-in (no Kafka required) | Built-in CDC for select sources |
| Database CDC sources | MySQL, Postgres, SQL Server, Oracle, MongoDB, DB2, others | SQL Server, Synapse, Postgres, MySQL |
| Streaming queues | Kafka, EventHubs, Kinesis, SQS, PubSub, ActiveMQ, RabbitMQ | Event Hubs |
| IoT brokers | MQTT brokers | IoT Hub |
| Real-time replication | Log-based CDC, full, incremental | Log-based CDC, full, incremental |
| Change tracking modes | Log-based, trigger-based, timestamp/high-watermark | Log-based, change tracking |
| Developer experience | ||
| REST API | full API for flows, connections, schedules, and runs | cloud API |
| CLI | full CLI with built-in SQL | cloud CLI |
| MCP server | built-in — connect Cursor, Claude, or ChatGPT to your instance | |
| Client libraries | Python, Bash, and PowerShell clients | cloud SDKs in many languages |
| Version control | built-in — automatic history, diff, and revert on every artifact | Partial — infrastructure-as-code, not artifact history |
| Embeddable / white-label | ||
| Compliance & security | ||
| SOC 2 Type 2 | audited; report under NDA, SOC 3 public | plus ISO 27001, PCI, FedRAMP |
| HIPAA | supported with a BAA | covered by the cloud BAA |
| GDPR / DPA | compliant, DPA available | |
| SSO and MFA | SAML SSO, optional 2FA, JWT stateless auth | cloud IAM and directory integration |
| Role and artifact-level access | six roles plus tag-based scoping of flows, connections, and schedules | fine-grained IAM policies |
| Encryption and data handling | TLS in transit, encrypted at rest, customer-managed PGP, SSH tunnels, IP allowlisting; rows are not persisted by default | platform KMS, customer-managed keys |
| Audit logging | admin actions logged, access logs monitored | cloud-native audit trail |
| Security testing | monthly vulnerability and penetration scans, static analysis blocking every build, periodic third-party audits | continuous, platform-wide |
| Gen AI | ||
| AI agent | Built-in agent (Simba) — builds and edits flows from chat | Partial — Copilot in Fabric (broader Microsoft AI) |
| Agent capabilities | Reads metadata, reads/samples data, writes JS & SQL, schedules, deploys, monitors | SQL/code suggestions in Fabric notebooks |
| Natural-language flow building | ‘Vibe-build’ — create flows by describing what you want | Partial — pipeline copilot in Fabric |
| AI-driven mapping | Auto-suggests source-to-destination mappings | Partial |
| Built-in analytics | Agent runs analysis on flow data and pipeline behavior | via Fabric / Power BI |
| Chat across product | Same agent context on every screen | Limited to Fabric experience |
| CLI for agent | Full CLI access for run/deploy/monitor/manage | |
| Trains on customer data | Never | Per Microsoft enterprise terms |