NATS integration
NATS data, in your warehouse.
A built-in ingestr connector: add credentials, pick tables, schedule it.
name: raw.natsmsgid
type: ingestr
parameters:
source_connection: nats
source_table: 'natsmsgid'
destination: snowflake
incremental_strategy: merge$ bruin run assets/raw/nats.asset.yml
- extract · NATS natsmsgidincremental
- load · snowflake raw.natsmsgidmerged
- checks · not_null, unique
Loaded and checked. Downstream models can run.
How it connects
Connected in three steps.
NATS is a messaging system with a persistent streaming layer called JetStream. ingestr reads messages from JetStream streams in finite batch runs or continuously with streaming ingestion. JetStream must be enabled on the NATS server. Core NATS subjects without a backing JetStream stream are not supported because they cannot replay messages or acknowledge them after a destination write.
- 01
Add a NATS connection with its credentials.
- 02
Pick the tables to load and how: replace, append or merge.
- 03
Bruin runs it on your schedule and checks every load.
Connection parameters
- stream
- JetStream stream-name fallback for embedded callers. The CLI still requires source-table.
- subject
- Subject or wildcard filter within the stream. Defaults to >.
- durable
- Durable consumer name used by streaming ingestion. If omitted, ingestr derives one from the stream name.
- token
- NATS authentication token.
- batchsize
- Maximum messages fetched at once. Defaults to 3000.
- batchtimeout
- Maximum fetch wait in seconds. Defaults to 5 and accepts fractional seconds.
Tables
3 tables, ready to load.
natsmsgiddatanats
The platform
Part of the Bruin platform.
Data in, ready for everything downstream: the models, the checks, the lineage and the AI layer.
01 · Move
02 · Model & govern
03 · Use
Streams & queues
More tools, same category.
Frequently asked
Questions about NATS.
Does Bruin have a built-in NATS integration?
Yes. Built-in ingestr source.
Which NATS tables can Bruin load?
natsmsgid, data, nats.
Where can NATS data go?
Snowflake, BigQuery, Databricks, Redshift, ClickHouse, Postgres, DuckDB, MotherDuck, Microsoft Fabric and more, plus files on S3 and GCS.
How fresh is the data?
As fresh as your schedule. Incremental loads append, merge or replace a time window, every few minutes if you like.
Do we need Bruin Cloud?
No. The Bruin CLI and ingestr run locally, in CI or in your own orchestrator. Bruin Cloud adds scheduling, lineage, alerts and the AI data analyst on top.
Ready to connect NATS?
$100 in credits and 50 AI tasks. No credit card.
A demo walks through your own data.