Snapshot
Continue to About ↓- What it does
- Feldera is an incremental view maintenance engine that keeps SQL views continuously in sync with changing data.
More detail
- It recomputes only the parts of a query affected by each insert, update, or delete, so results stay fresh while compute scales with the size of the change rather than the dataset.
- It works alongside your lakehouse or warehouse and is not a database or stream processor itself.
- Best for
- Data teams running complex SQL over rapidly changing data — such as medallion gold layers, fine-grained authorization checks, or fraud feature pipelines — who need fresh results without full batch recomputation.
More detail
- Also fits regulated or self-hosted environments where data must stay inside the perimeter.
- Pricing
- Open-source edition is $0 under the MIT license (single node, full SQL and all connectors, community support only)
More detail
- you pay your own infrastructure and staff costs.
- Enterprise edition is a custom quote, not published, deployed on your own Kubernetes and adding scale-out, fault tolerance, enterprise
- Main limitation
- Teams needing a fully managed cloud service with published pricing or multi-node scale-out today
More detail
- teams requiring independent third-party reviews (none were found in the evidence)
- or single-node users who need fault tolerance, enterprise security, and dedicated support out of the box, which require the Enterprise edition.
- Record
- Living and monitored
- Reviewed
- Evidence
- Public web research
- Machine access
- Structured profile ↗
About
- Feldera is an incremental view maintenance engine that keeps SQL views continuously in sync with changing data.
- It recomputes only the parts of a query affected by each insert, update, or delete, so results stay fresh while compute scales with the size of the change rather than the dataset.
- It works alongside your lakehouse or warehouse and is not a database or stream processor itself.
What it helps with
- Stale answers from batch recomputation, where gold layers lag hours behind source changes.
- Runaway warehouse compute bills caused by full recomputation over large datasets.
- Engineering waste from brittle ad-hoc workarounds for keeping complex SQL fresh.
- Permission and authorization checks over deeply nested access graphs that must reflect current data.
- Per-transaction fraud features that need to reflect what just happened, not yesterday's batch.
Capabilities
A practical view of what is available and where limitations apply.
| Offering | Available | Detail |
|---|---|---|
| Incremental view maintenance over SQL | Yes | Keeps arbitrarily complex SQL views in sync by recomputing only what changed; state is guaranteed to match a full batch rerun over the same inputs (docs.feldera.com). |
| Full SQL support | Yes | Joins, aggregates, DISTINCT, window functions, JSON, arrays/maps, recursive views, UDFs, correlated subqueries (docs.feldera.com). |
| Deeply nested views at scale | Yes | Official site shows examples with 100+ SELECTs, 500+ joins and 700+ aggregates maintained incrementally. |
| Connectors | Yes | Delta Lake, Iceberg, Postgres, Kafka, HTTP, NATS, S3, and custom connectors per the official site. |
| Datasets larger than RAM | Limited | Docs state spilling to NVMe and S3 is available, but the open-source edition is single-node only; scale-out across nodes is not available. |
| Multi-node scale-out | No | Pricing page lists scale-out as not available; Enterprise page describes it as coming soon. |
| Fault tolerance | Limited | Enterprise only, and the docs describe fault tolerance as a preview feature requiring connector support. |
| Enterprise SSO, RBAC, OIDC, secrets, per-pipeline isolation | Yes | Listed as Enterprise-only features on the pricing page. |
| Ad-hoc queries against materialized views | Yes | Uses Apache DataFusion; docs note ad-hoc results can differ slightly from the incremental engine in dialect and rounding. |
| Published Enterprise pricing | No | Pricing is custom and requires a demo request; no list price is published. |
Bot trust
How confidently AI systems can verify this profile from public evidence.
—of 100
ChatGPT—/100
Claude—/100
This profile has not completed a Bot Score assessment yet.
Evidence
Where this record came from, when it was reviewed, and how confidently it can be used.
- Verification
- Scribble researched these facts from the linked public sources.
- The owner has not manually confirmed every field.
- Last reviewed
- Primary source
- feldera.com
Technical profile details
Record ID
b160ad97-26ed-4512-ae99-319b13e13581Canonical URLhttps://profiles.scribble.network/listing/feldera-b160ad97