Azure Data Factory alternative — Azure Data Factory vs Skippr
Azure Data Factory is pipeline orchestration. A pipeline is activities on an Integration Runtime: Azure (serverless), Azure Managed VNET, or Self-Hosted. You pay three meters. Orchestration is per 1,000 activity runs (about 1 USD on cloud IR in US East). Copy is per DIU-hour (about 0.25 USD on Azure IR). Mapping Data Flows are per vCore-hour, 8 vCores minimum, prorated by the minute and rounded up (General Purpose around 0.27 USD per vCore-hour on the October 2026 US East list). CDC artefacts run on a single-node 4-vCore Data Flow cluster at General Purpose rates while CDC is in public preview. Operations (create, monitor) are a fourth line. The first five low-frequency activities a month are free. Iceberg is a Copy sink on Azure Data Lake Storage Gen2 only (IcebergSink). Synapse pipelines share the same connector table. Managed Airflow (Workflow Orchestration Manager) stopped new instances on 1 January 2026. This is not a Streamhouse. There is no Iceberg ∪ WAL.
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 DIU. ELT into a warehouse is one path, not the product.
| Feature | Skippr | Azure Data Factory |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | Azure IR, Managed VNET IR, or Self-Hosted IR |
| Schema | In-stream evolution, sibling columns | Dataset mapping. Iceberg sink on ADLS Gen2 |
| Storage | Open Iceberg on Parquet you own | Linked service dest. Iceberg is Copy-to-ADLS only |
| CDC | Log-based; fails closed if the dest cannot honour | CDC artefacts on a 4-vCore Data Flow node (preview rates) |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | Synapse / Fabric / warehouse after the pipeline |
| Pricing | Free self-hosted engine | Runs plus DIU-hours plus vCore-hours. First 5 low-frequency activities free. |
What Azure Data Factory is good at
A pipeline that already copies on-prem SQL into ADLS and kicks a Databricks notebook. That is the product. Self-Hosted IR is why hybrid estates stay. IcebergSink on Gen2 is a real dest if you already run ADF. If the job is "orchestrate Azure copies," that is a real product.
Where the job is wider
ADF is a control plane. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The pipeline ends; then you query Synapse or Fabric.
Three meters is the ops. A Copy plus a Data Flow plus a trigger is three lines. Managed VNET pipeline-activity hours sit far above cloud IR. CDC is a preview cluster, not a fail-closed WAL tail. Skippr will not run an ADF pipeline. Skippr also will not replace a Self-Hosted IR that already talks to a plant network.
If the job is "Azure orchestration we already staff," ADF is the narrower tool. If the job is live SQL on Iceberg you run, ADF 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.
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: ${APP_DB_PASSWORD}
database: app
tables: [orders, customers]
cdc_mode: snapshot_then_cdc
data_sinks:
lake:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1
schema_sink: schema_sinks.lake_schema
schema_sinks:
lake_schema:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1skipprd --workspace analytics --storage-mode local connect data-source postgres \
--pipeline orders \
--name app_db \
--host db.internal \
--user skippr_reader \
--password '${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_schemafrom 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 Azure Data Factory if you need Azure pipeline orchestration and a warehouse after the activity is enough.
Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not run an ADF pipeline.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.