Matia alternative — Matia vs Skippr

Matia is a SaaS DataOps bundle: managed ELT, reverse ETL, and observability under one login. A catalog is on the pricing page as coming soon. ETL is billed on monthly active rows, same unit as Fivetran. They say they do not count deletes. Starter starts at $1,250 a month: 10 users, hourly ETL, CDC, one reverse-ETL destination on an 8-hour cycle, 100 monitored tables, five custom monitors. Standard adds 15-minute ETL, two destinations, SSO. Enterprise goes to 5-minute ETL and unlimited monitors. About 150 connectors. They advertise Fivetran-pipeline compatibility.

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 MAR floor. ELT into a warehouse is one path, not the product.

Feature	Skippr	Matia
Deploy	Single Rust binary, your VPC or on-prem	SaaS. AWS Private Link on higher plans
Schema	In-stream evolution, sibling columns	Managed connectors, column blocking, hashing on Standard
Storage	Open Iceberg on Parquet you own	Destination warehouse (Snowflake, BigQuery, Redshift, Databricks, S3)
CDC	Log-based; fails closed if the dest cannot honour	CDC on Starter. They highlight Postgres and MongoDB
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Warehouse after the load. Observability is table monitors
Pricing	Free self-hosted engine	From $1,250/month plus MAR. See their pricing.

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What Matia is good at

One contract instead of Fivetran plus Census plus Monte Carlo. Reverse ETL and freshness monitors sit next to the ingest job. Deletes stay off the MAR counter, which is kinder than vendors who bill them. Dedicated Slack on Starter. Custom connectors in a day or two, they say. If you want that bundle and you already think in MAR, Matia is a real product.

Where the job is wider

MAR is still the meter. Chatty tables still move the bill. $1,250 is the floor before rows. Reverse ETL destinations are capped on Starter and Standard. Observability is capped by table and custom-monitor counts. The catalog they sell as part of the story is still coming soon on the public pricing page.

Matia is cloud-only. There is no binary in your VPC. Query waits for the warehouse. There is no Iceberg ∪ WAL. Skippr will not give you Matia's reverse-ETL debugger or their table monitors. If the job is "ingest, activate, and watch 100 tables in one SaaS login," Matia is the narrower tool. If the job is live SQL on Iceberg you run, Matia 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 Matia if you want ingest, reverse ETL, and table monitors in one SaaS bill, and MAR without deletes is acceptable.

Choose Skippr if you want one binary, no MAR floor, 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.