Quick answers to common questions about Etlworks. For deeper topics, follow the links to the relevant guides.
About Etlworks
Etlworks LLC is headquartered in Pittsburgh, Pennsylvania. Etlworks is a modern, cloud-first, any-to-any data integration platform. It connects business applications, databases, files, APIs, and message queues; you build, test, schedule, and operate the pipelines right in the browser using a drag-and-drop canvas (Composer), natural-language prompts (Simba), scripting, or SQL.
Why do I need Etlworks?
Use Etlworks if you need to:
- Connect to data wherever it lives — databases, files, APIs, SaaS apps, message queues, on-premise or in the cloud.
- Move, transform, and synchronize data without writing custom code (or with only a few lines when you do).
- Track changes in transactional databases and replicate them to a warehouse in near real-time (CDC).
- Expose your data as APIs that other applications can call.
- Schedule and orchestrate complex multi-step workflows with conditions, loops, parallel branches, and error handling.
Etlworks is built for the cloud and runs equally well on-premise. It supports billions of records, 270+ connectors, real-time streaming, and CDC from every major database.
Compare Etlworks to other ETL platforms →
How do I work with my data in Etlworks?
Three common starting points:
- Vibe-build a flow with Simba. Describe what you want in plain English — for example, "load Stripe charges into Snowflake every hour". Simba builds the flow on the Composer canvas, with your approval at each state-changing step. More on Simba →
- Build it manually in Composer. Drag connections onto the canvas, wire source to destination, attach transformations and schedules. More on Composer →
- Start from a template. The template library has hundreds of ready-made flows you can adapt to your scenario.
Extract
Extraction means pulling data out of a source. A source connection can be a database, a file system (local, S3, Azure Blob, GCS, etc.), an HTTP API, a mail server, a message queue, or a SaaS app. Define the source connection, point a flow at it, and Etlworks handles the read.
Transform
Transformations include field mapping, calculated fields, format conversion, filtering, joins, deduplication, denormalization, nested-to-flat reshaping, and more. Etlworks supports field-level mapping in a visual editor as well as JavaScript, Python, and SQL for custom transformations.
More on transformations and mapping →
Load
The destination connection is the load target. Any supported connector can be a destination: databases, warehouses (Snowflake, Redshift, BigQuery, Synapse, Vertica, Greenplum), files, APIs, message queues. For warehouses, Etlworks supports bulk-load patterns optimized for high-volume loads.
Does Etlworks store my data?
No. Etlworks connects sources and destinations directly (point-to-point). Data flows through the engine in memory or in temporary staging when a flow requires it; it is not persisted on Etlworks infrastructure beyond what the flow itself dictates. This lets us work with financial-services, healthcare, and other security-sensitive customers.
How do I integrate Etlworks into my application or workflow?
Four common patterns:
- REST API. Manage flows, connections, schedules, executions, and audit via REST. Developer documentation →
- AI Agent API. Invoke Simba as a subagent from your own application or AI toolchain (LangChain, CrewAI, AutoGen, etc.). AI Agent API documentation →
- Webhooks. Subscribe to events in Etlworks and have the platform POST to your URL when they fire. More on webhooks →
- Embedded UI. Embed parts of the Etlworks UI — for example, the connection editor — inside your own web app via iframe.