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Coupler.io alternative — Coupler.io vs Skippr ​

Coupler.io is no-code SaaS for marketing and ops. 400+ sources (ads, Shopify, Sheets, SaaS) into Google Sheets, Excel, Looker Studio, Power BI, BigQuery, Snowflake, or Redshift. You pay for unique accounts (one set of credentials) and how many destination apps you use. Free is one account, one destination, manual refresh, 100 rows. Starter is 32 USD a month (24 on annual) for three accounts, one destination, daily refresh, 5,000 rows per run. Active is 132 (99 annual) for 15 accounts and three destinations. Pro is 259 (199 annual) for 50 accounts, unlimited destinations, hourly refresh. Custom goes to 15 minutes. A 7-day trial, then Free or a paid plan. All sources are on every plan.

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 account pack. ELT into a warehouse is one path, not the product.

FeatureSkipprCoupler.io
DeploySingle Rust binary, your VPC or on-premSaaS only
SchemaIn-stream evolution, sibling columnsIn-app transforms, filters, column rename before the load
StorageOpen Iceberg on Parquet you ownSheets, Excel, BI, or a warehouse dest. Starter is one dest app
CDCLog-based; fails closed if the dest cannot honourScheduled refresh. Incremental by timestamp or append. Not log CDC
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineDestination after the refresh. In-app dashboards on paid plans
PricingFree self-hosted engineFree 100 rows. Paid from 24 USD a month annual. See their pricing.

What Coupler.io is good at ​

Sheets and Looker Studio without a warehouse. Ads plus Shopify into one tab, daily, on a bill a marketing lead can approve. Templates and source blending are the product. BigQuery is a destination on every plan, not an add-on. If the job is "stop pasting CSVs into the weekly deck," that is a fair product.

Where the job is wider ​

Accounts and destinations are the wall. Starter locks you to one dest app: pick Sheets and you cannot also land BigQuery until you rewrite or upgrade. Exceed the row cap and the run still "succeeds" with a truncated set. They count rows at the source, before your filters. Append to a database does not dedupe. There is no log CDC. Fastest published refresh is 15 minutes on Custom.

Coupler.io is cloud-only. There is no binary in your VPC. Query waits for Sheets or the warehouse. There is no Iceberg ∪ WAL.

Skippr will not give you Coupler.io's Looker Studio connector or their Shopify dashboard templates. If the job is "Google Ads into Sheets this afternoon," Coupler.io is the narrower tool. If the job is live SQL on Iceberg you run, Coupler.io 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.

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 Coupler.io if you want ads and SaaS in Sheets or Looker Studio on a small monthly bill, and daily or hourly is live enough.

Choose Skippr if you want one binary, no account or row cap, 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.