What we're building next. These are planned capabilities, not yet available in the platform.
Two AI-assisted features on our roadmap, aimed at cutting the time engineers spend debugging failures and learning unfamiliar systems.
It helps you understand why the pipeline failed, where the root cause is, and what you can do to fix it.
Imagine connecting to a technology you have never worked with before — Kafka, MQ, Iceberg, a Graph Database, a Document Database, or another modern data platform — and instead of spending hours searching documentation, the platform guides you through the connection, required parameters, authentication, common mistakes, and recommended configuration.
Automated conversion from legacy visual ETL tools (e.g. Informatica, NiFi, Talend) into DataKnits pipelines. Not yet available — here's what's planned.
Parse existing legacy ETL job definitions (XML/JSON-based) and identify the sources, transforms, and destinations they encode.
Rebuild the analyzed legacy job as a DataKnits visual pipeline flow, ready to review and compile to native code rather than a proprietary runtime.
The goal is to let teams retire expensive legacy visual-tool licenses once migrated, without hand-rewriting every pipeline from scratch.
Tell us what would help your team most — roadmap priorities are shaped directly by user feedback.
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