ETL & pipelines.
Visual and code-first pipelines with scheduling, lineage and schema management.
Build and orchestrate data pipelines visually or in code, with scheduling, retries, schema management and end-to-end lineage — so an analyst can trust where every number came from.
- Category
- Analytics
- Deployment
- Managed → air-gapped
- Governance
- IAM · encryption · audit
ELT solved loading. It didn't solve trust.
Modern pipelines made it easy to move data from everywhere into the warehouse and transform it in place. What stayed hard is trusting the number at the end: when a dashboard looks wrong, the question is which source, which transform, and which silent schema change three steps back caused it. Aether's pipelines carry column-level lineage from source to dashboard, so that question has an answer — every field traces to where it came from and what happened to it on the way.
Visual for the analyst, code for the engineer — one runtime.
An analyst builds a transform on a canvas; an engineer writes it in the SDK; both run on the same engine with the same scheduling, retries and lineage. There's no rewrite when a prototype becomes production, and no second orchestration system bolted on for the parts the visual tool couldn't express. Connectors across databases, SaaS and files are managed, and reverse ETL pushes governed data back out to the operational tools that need it.
Schema drift handled, not discovered in production.
Upstream schemas change — a column is renamed, a type widens, a field disappears. The difference between a robust pipeline and a 2 a.m. page is whether that drift is detected and handled or silently propagated until a downstream report breaks. Aether detects schema drift and applies your policy — quarantine, alert, or evolve — instead of failing quietly. And because the pipeline runs next to the model, an enrichment step can call Aether inline to classify, extract or clean as the data flows.
A pipeline step that enriches rows with the model inline — classification as part of the transform, not a separate system.
pipeline("tickets")
.source("zendesk.tickets") // managed connector
.transform((t) => ({
...t,
category: aether.classify(t.body, // model, inline in the pipeline
["bug", "billing", "feature", "other"]),
}))
.onSchemaDrift("quarantine") // not a silent break
.load("lake.tickets_enriched"); // lineage tracked end to endThe classification is a transform step, not an export to an external model and back. Schema drift is a declared policy, not a production incident. And the lineage from Zendesk to the enriched table is tracked, so a wrong number downstream has a traceable cause.
Three steps to running.
Managed connectors across databases, SaaS and files, visual or code-first.
Build transforms with scheduling, retries and schema-drift handling.
Column-level lineage traces every number to its source.
ETL & pipelines, in full.
Drag-and-drop for analysts, SDK for engineers, one runtime.
Managed sources and sinks across databases, SaaS and files.
Column-level lineage from source to dashboard for trust and audit.
Detect and handle schema drift instead of breaking silently.
Provision it in a few lines.
Every service is reachable from the same SDK, CLI and infrastructure-as-code — one identity, one bill, one audit trail across the whole catalog.
# Provision etl & pipelines on Aether Cloud
aether analytics create \
--service etl-pipelines \
--name app \
--region us-1 \
--deploy managed # or vpc | on-prem | air-gappedAt a glance.
- Authoring
- Visual + code (SDK)
- Connectors
- Databases · SaaS · files
- Lineage
- Column-level
- Drift
- Detected and handled
- Direction
- ETL + reverse ETL
Built for real work.
Warehouse loading
SaaS data integration
Reverse ETL to operational tools
On one model, not stitched together.
The usual stack runs etl & pipelines in one product, the model in another and the data in a third — and the seams between them are the cost. Aether Cloud runs it on the same platform that serves the model, governs your identity and deploys into your boundary, with the rest of the catalog one hop away.
No stitching a vector DB to one place, a warehouse to another and a model to a third — etl & pipelines sits next to the rest of the catalog, one identity, one bill.
The provider that runs Aether runs your etl & pipelines — so the data and the model never leave the same governed boundary to talk to each other.
Need a capability that isn’t here yet? The model writes and deploys it into the same boundary — the catalog is a starting point, not a ceiling.
Good to know.
Managed connectors and transforms with column-level lineage — loading the warehouse and lake on one platform.
Yes — drift is detected and handled instead of silently breaking.
Yes — reverse ETL syncs governed data back to operational systems.
Your boundary, your choice.
Pairs well with.
Managed Spark and distributed processing for large-scale transforms and ML feature pipelines.
Exactly-once streaming compute for real-time pipelines, enrichment and detection.
Governed dashboards and self-serve exploration over the warehouse and lake.
Full-text and hybrid search as a service, with relevance tuning and analyzers.
Run ETL & pipelines on Aether Cloud.
Visual and code-first pipelines with scheduling, lineage and schema management. Deployable managed, in your VPC, on-prem or fully air-gapped — talk to us about the configuration your workloads and your boundary require.