Bruin alternative — Bruin vs Skippr

Bruin is a Git-native data platform. SQL and Python assets, quality checks, lineage, and YAML dashboards live in the repo. Ingestion is ingestr: one CLI, 130+ sources, 20+ destinations. Self-hosted is free. ingestr is source-available under the Functional Source License, not Apache or MIT. Incremental loads use a cursor. Log-based CDC (Postgres, MySQL, SQL Server, MongoDB) takes a snapshot then --stream. Bruin Cloud is optional: 100 USD of credits and 50 AI tasks to start, then 0.01 USD a credit. Ingestion and SQL assets bill from 2.50 USD an hour, per second while they run.

Skippr is an open Streamhouse you run as one Rust binary. Write skippr.yml (or the same thing in Python), discover, sync. You query live state over Iceberg ∪ WAL, and the history is ordinary Iceberg in your bucket. No credit meter. ELT into a warehouse is one path, not the product.

Feature	Skippr	Bruin
Deploy	Single Rust binary, your VPC or on-prem	CLI on your machine or CI. Cloud is managed. VPC on request
Schema	In-stream evolution, sibling columns	Asset YAML plus destination types. Quality checks in the repo
Storage	Open Iceberg on Parquet you own	Warehouse or database you point `ingestr` at
CDC	Log-based; fails closed if the dest cannot honour	Cursor incremental. Log CDC and `--stream` on supported databases
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	Destination after the asset. Dashboards as YAML
Pricing	Free self-hosted engine	Self-host free. Cloud from 2.50 USD an hour. See their pricing.

What Bruin is good at

One repo for ingest, SQL, Python, and checks. ingestr is a short command: source URL, dest URL, table. No Kafka required for the CDC path they shipped. Cloud has no seat fee. For a team that already thinks in assets and wants GitHub Actions or Bruin Cloud to run them, that is a real product.

Where the job is wider

FSL is not OSI. Read it before you embed ingestr in a competing SaaS. Cloud credits do not roll the same way a binary does: a chatty SQL asset still burns 250 credits an hour. Their own ingest FAQ still says pair a dedicated CDC tool if you need sub-second; --stream is the newer exception on a few databases.

Bruin is a platform around warehouse assets. It is not a Streamhouse. There is no Iceberg ∪ WAL. Query waits for the destination. Skippr will not give you Bruin's YAML dashboards, asset quality DSL, or their Cloud AI tasks. If the job is "Git-native SQL plus a copy CLI," Bruin is the narrower tool. If the job is live SQL on Iceberg you run, Bruin was never that.

Getting started

Install skipprd from the install guide, or pip install skippr. The three tabs write the same skippr.yml. Then discover, sync, and query.

YAMLCLIPython

yaml

skippr:
  workspace: analytics
  skipprd_el_storage_mode: local

pipelines:
  orders:
    data_source: data_sources.app_db
    data_sink: data_sinks.lake

data_sources:
  app_db:
    Postgres:
      host: db.internal
      user: skippr_reader
      password: ${APP_DB_PASSWORD}
      database: app
      tables: [orders, customers]
      cdc_mode: snapshot_then_cdc

data_sinks:
  lake:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1
    schema_sink: schema_sinks.lake_schema

schema_sinks:
  lake_schema:
    AthenaIceberg:
      warehouse: s3://my-iceberg-warehouse/
      glue_database_name: analytics
      athena_workgroup_name: primary
      athena_results_s3_bucket: my-athena-results
      region: us-east-1

bash

skipprd --workspace analytics --storage-mode local connect data-source postgres \
  --pipeline orders \
  --name app_db \
  --host db.internal \
  --user skippr_reader \
  --password '${APP_DB_PASSWORD}' \
  --database app \
  --tables orders \
  --tables customers \
  --cdc-mode snapshot_then_cdc

skipprd connect data-sink athenaiceberg \
  --pipeline orders \
  --name lake \
  --warehouse s3://my-iceberg-warehouse/ \
  --glue-database-name analytics \
  --athena-workgroup-name primary \
  --athena-results-s3-bucket my-athena-results \
  --region us-east-1

skipprd connect schema-sink athenaiceberg \
  --pipeline orders \
  --name lake_schema

python

from skippr import (
    Config,
    DataSinkAthenaIceberg,
    DataSourcePostgres,
    DataSourcePostgresSourceCdcMode,
    EnvRef,
    LocalStorage,
    Pipeline,
)

cfg = Config().workspace("analytics").storage(LocalStorage())

app_db = cfg.data_source(
    "app_db",
    DataSourcePostgres(
        host="db.internal",
        user="skippr_reader",
        password=EnvRef("APP_DB_PASSWORD"),
        database="app",
        tables=["orders", "customers"],
        cdc_mode=DataSourcePostgresSourceCdcMode.SNAPSHOTTHENCDC,
    ),
)

lake = cfg.data_sink(
    "lake",
    DataSinkAthenaIceberg(
        warehouse="s3://my-iceberg-warehouse/",
        glue_database_name="analytics",
        athena_workgroup_name="primary",
        athena_results_s3_bucket="my-athena-results",
        region="us-east-1",
    ),
    schema_sink="lake_schema",
)

cfg.pipeline("orders", Pipeline(data_source=app_db, data_sink=lake))
cfg.save("skippr.yml")

bash

export APP_DB_PASSWORD='your-postgres-password'
skipprd discover --pipeline orders
skipprd sync --pipeline orders --once
skipprd query "SELECT count(*) FROM analytics.orders"

Which to choose

Choose Bruin if you want Git-native SQL and Python assets plus ingestr, and a warehouse after the run is enough.

Choose Skippr if you want one binary, no credit meter, live SQL, and Iceberg files that stay yours.

Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.

Bruin alternative — Bruin vs Skippr ​

What Bruin is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Bruin alternative — Bruin vs Skippr

What Bruin is good at

Where the job is wider

Getting started

Which to choose