dltHub alternative — dlt vs Skippr

dlt is an Apache-2.0 Python library. You write a pipeline in code: source, destination, incremental cursor, schema inference, flatten. Same script can land DuckDB, Postgres, Snowflake, BigQuery, or files. Cron, GitHub Actions, or Airflow is yours. dltHub is the paid plane that runs those scripts: managed runtime, quality checks, transforms, and an Iceberg / Delta destination under a commercial licence. Published Hub price is 12,000 USD a month, 5,000 credits included, 12-month minimum. A credit is one hour of standard runtime. Trial is 14 days with 30 USD of credits.

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 12,000 USD floor. ELT into a warehouse is one path, not the product.

Feature	Skippr	dlt / dltHub
Deploy	Single Rust binary, your VPC or on-prem	Library in your process. Hub is a managed runtime
Schema	In-stream evolution, sibling columns	Inferred and evolved in the pipeline. Nested types flatten
Storage	Open Iceberg on Parquet you own	Warehouse, DuckDB, files on OSS. Iceberg / Delta on Hub
CDC	Log-based; fails closed if the dest cannot honour	Incremental cursors. SQL Server is a Hub premium source
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Destination after the load. pipeline.dataset() is DuckDB views on a snapshot
Pricing	Free self-hosted engine	Library is free. Hub is 12,000 USD a month. See their pricing.

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What dlt is good at

A pipeline you can read. pip install dlt, a REST source, DuckDB on a laptop, then change one argument for Snowflake. Schema inference and incremental state live in the repo. Verified sources cover common APIs. For a Python team that wants extract-load as a library, not a SaaS login, that is a real product. The library stays Apache-2.0 if you never buy Hub.

Where the job is wider

You own the run. Secrets, retries, alerts, and the hour the job dies on a Friday are yours unless you pay Hub. Hub is 12,000 USD a month before extra credits. Iceberg and Delta destinations sit on that commercial licence, not on the OSS default. Their Iceberg merge notes orphaned nested rows when a parent update drops children. That is published, not a guess.

dlt is a loader. It is not a Streamhouse. pipeline.dataset() is DuckDB on the last snapshot. There is no skipprd query over in-flight WAL. Python only.

Skippr will not give you a library you import into a notebook, or Hub's quality dashboard. If the job is "twelve lines of Python into DuckDB," dlt is the narrower tool. If the job is live SQL on Iceberg you run, dlt 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 dlt if you want a Python library in your repo, you will run the job, and DuckDB or a warehouse after the load is enough.

Choose Skippr if you want one binary, no Hub 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.