Mozart Data alternative — Mozart Data vs Skippr

Mozart Data is a managed modern data stack. They provision Fivetran (and Portable for long-tail SaaS) to load a Snowflake or BigQuery warehouse they run for you. SQL and dbt Core sit on top. You get connectors, a warehouse, transforms, and analyst hours as one subscription. Sonata is free for 250,000 monthly active rows and 15 compute-hours, then $3 a compute credit and $67.50 per 100,000 MAR. Concerto is $1,200 a month plus a $1,000 implementation fee for 1.75M MAR. Symphony and Opera go to $3,000 and $6,000. Annual billing knocks those down. Extra rows and extra compute are still metered.

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 plus warehouse-hour invoice. ELT into a warehouse is one path, not the product.

Feature	Skippr	Mozart Data
Deploy	Single Rust binary, your VPC or on-prem	Managed SaaS. They run Fivetran and the warehouse
Schema	In-stream evolution, sibling columns	Fivetran schema handling, then SQL / dbt models you write
Storage	Open Iceberg on Parquet you own	Their Snowflake or BigQuery account. Storage overages on Snowflake
CDC	Log-based; fails closed if the dest cannot honour	Whatever Fivetran offers on that source. No Mozart-native CDC engine
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Warehouse SQL after the sync. Metabase is included. BI is yours
Pricing	Free self-hosted engine	Free tier, then MAR + compute-hours + $1,000 implementation on paid plans. See their pricing.

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What Mozart Data is good at

They sell the stack, not a single connector. A growth-stage team can land Salesforce, Stripe, and Postgres in Snowflake without hiring a data engineer or signing three vendor contracts. Unlimited users and connectors on the published plans. Dedicated analyst hours on Symphony and Opera. Reviewers like the setup speed and the support. If the job is "stand up a warehouse and get someone on the phone," that is a real product.

Where the job is wider

You are paying Fivetran MAR and warehouse compute through Mozart's meter. Sonata overages are $3 a Snowflake credit and $67.50 per 100,000 MAR. Paid plans add a $1,000 implementation fee before the first pipeline. Portable flows for long-tail sources are a separate line on the MSA. You do not pick a different ingest engine. You do not run the warehouse yourself unless you leave.

Mozart is not an engine. There is no binary in your VPC. Query is Snowflake or BigQuery after Fivetran lands. There is no Iceberg ∪ WAL. Native dashboards are thin; they point you at Metabase or your own BI. CDC is Fivetran's, with Fivetran's limits.

Skippr will not give you Mozart's analyst hours or a provisioned Snowflake account. If the job is "managed stack plus a person who writes the first models," Mozart Data is the narrower tool. If the job is live SQL on Iceberg you run, Mozart 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 Mozart Data if you want Fivetran plus a warehouse plus someone to help write the first SQL, and you will pay MAR and compute-hours for that bundle.

Choose Skippr if you want one binary, no MAR plus warehouse 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.