Hightouch alternative — Hightouch vs Skippr

Hightouch is reverse ETL plus a composable CDP. Basic Reverse ETL is free: up to 2 active syncs, unlimited destinations, unlimited seats. Paid is usage-based and sales-led: Customer Studio, real-time personalization, identity resolution, Match Booster, and an Agentic Marketing / Lifecycle Studio line. They say no MTU cap, no seat cap, no source or destination cap on Composable plans. Destinations are 300-plus (instances of the same dest count once). Sources are warehouses (Snowflake, BigQuery), databases, CSV, SFTP, or a BI tool. Iceberg is not a Hightouch writer. A warehouse that already queries Iceberg can be a source. This is warehouse-out, not Iceberg ∪ WAL you query in one binary.

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. Skippr will not sync Braze. ELT into a warehouse is one path, not the product.

Feature	Skippr	Hightouch
Deploy	Single Rust binary, your VPC or on-prem	Hightouch Cloud (EU region available on paid)
Schema	In-stream evolution, sibling columns	Warehouse SQL / audiences. Mapping in the sync
Storage	Open Iceberg on Parquet you own	Reads your warehouse. Writes the SaaS dest
CDC	Log-based; fails closed if the dest cannot honour	Not CDC. Diff of modeled rows out to an app
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	The warehouse you already query. Hightouch is the push
Pricing	Free self-hosted engine	Free: 2 active syncs. Paid Composable / Agentic is a quote.

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What Hightouch is good at

SQL in the warehouse, a sync into the marketing tool, audiences on top, without standing a packaged CDP copy of the customer. That is the product. Warehouse-side planning (run the diff in Snowflake) is why some teams kept compute on their bill. If the job is "this model into this ad platform," that is a real product.

Where the job is wider

Hightouch is reverse ETL and activation. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Iceberg is only in play if the warehouse already serves it. Postgres CDC still lands somewhere else first. Skippr will not replace a Hightouch audience into Meta.

Syncs, the Hightouch workspace, and warehouse query cost are the ops. Skippr will not run Hightouch. Census, RudderStack, and Segment are separate pages.

If the job is "warehouse into the campaign tool," Hightouch is the narrower tool. If the job is live SQL on Iceberg you run, reverse ETL 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

CLI

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")

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 Hightouch if you want warehouse-native reverse ETL (and, if you buy it, the composable CDP), and two free syncs or a quote is enough.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not sync Braze.

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