Microsoft Fabric alternative — Microsoft Fabric vs Skippr
Microsoft Fabric is a capacity SaaS. You buy an F SKU (or Power BI Premium). Data Factory in Fabric is the pipeline and Copy Job surface. Mirroring continuously replicates Azure databases, Snowflake, and other sources into OneLake. Core mirroring compute does not consume CUs. Replica storage is free at 1 TB per purchased CU (F64 is 64 TB). You still need a running capacity; a paused SKU stops replication and can bill OneLake for the replica. Query (SQL, Spark, Power BI) bills at normal CU rates. Snowflake Iceberg tables can shortcut into OneLake without copying Parquet; OneLake virtualizes them as Delta for Fabric engines. External, transient, temporary, and dynamic Snowflake tables are out. Granular Snowflake grants do not travel. Optional mirroring extensions (Delta change data feed, views) bill incremental work at 3 CU-hours on the DataMovementIncrementalCopy meter, per second, only when changes run. Idle and empty iterations are free. 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 F SKU. ELT into a warehouse is one path, not the product.
| Feature | Skippr | Microsoft Fabric |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | Fabric workspace on an F / Premium capacity |
| Schema | In-stream evolution, sibling columns | Copy Job mapping. Mirroring keeps the source shape |
| Storage | Open Iceberg on Parquet you own | OneLake (Delta). Snowflake Iceberg is a shortcut, then Delta virtualization |
| CDC | Log-based; fails closed if the dest cannot honour | Mirroring replica. CDF extension is 3 CU-hours when changes run |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | SQL analytics endpoint, Spark, Power BI on the capacity |
| Pricing | Free self-hosted engine | F SKU. Mirroring compute free; 1 TB replica per CU. Queries burn CUs. |
What Microsoft Fabric is good at
A warehouse that already lives next to Power BI on one capacity. That is the product. Mirroring is why some teams drop ADF copies for Azure SQL and Snowflake. Iceberg shortcuts keep Snowflake Parquet in place. If the job is "OneLake plus the semantic model," that is a real product.
Where the job is wider
Fabric is a SaaS lakehouse. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Iceberg in is virtualized to Delta for Fabric engines. Files you own after a pause still sit behind a capacity bill.
CU math is the ops. Mirroring compute is free until you query or turn on CDF. A paused F SKU stops the replica and can start storage charges. Skippr will not run a Fabric pipeline. Skippr also will not replace Direct Lake models.
If the job is "Microsoft estate we already staff," Fabric is the narrower tool. If the job is live SQL on Iceberg you run, Fabric 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 Microsoft Fabric if you want OneLake, mirroring, and Power BI on one capacity, and SQL after the replica is enough.
Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not run a Fabric pipeline.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.