SSIS alternative — SQL Server Integration Services vs Skippr

SQL Server Integration Services is package ETL. You design a control flow and a data flow in Visual Studio, save a .dtsx, and deploy the project to the SSISDB catalog. The catalog holds parameters, environments, and run history. On-prem that catalog lives on SQL Server. In Azure it lives on Azure SQL or Managed Instance and the packages run on an Azure-SSIS Integration Runtime: you pick a VM size and Standard or Enterprise, and you pay per node-second. Enterprise is the SKU that still ships CDC components, plus Oracle, Teradata, and SAP BW connectors. SQL Server 2025 (17.x) removed the Attunity CDC components, the Attunity Oracle CDC service, the Microsoft Connector for Oracle, and the Hadoop Hive / Pig / File System tasks. The legacy Integration Services Service and 32-bit mode are deprecated. Microsoft's next path is Fabric Data Factory. Invoke SSIS Package there is Preview: upload .dtsx to OneLake, no on-prem sources, no private endpoints, no custom components. SSIS is not a Streamhouse. There is no Iceberg destination in the box.

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

Feature	Skippr	SQL Server Integration Services
Deploy	Single Rust binary, your VPC or on-prem	SSDT plus SSISDB. Azure-SSIS IR or Fabric Invoke (Preview)
Schema	In-stream evolution, sibling columns	Package metadata. Column mapping is per data flow
Storage	Open Iceberg on Parquet you own	Destination you coded. No Iceberg task in the box
CDC	Log-based; fails closed if the dest cannot honour	Attunity CDC gone in SQL Server 2025. Azure-SSIS Enterprise still ships CDC components
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Target after the package. No lake SQL in SSIS
Pricing	Free self-hosted engine	SQL Server cores (not a separate SKU). Azure-SSIS IR is node-hour, Standard or Enterprise.

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What SSIS is good at

A data-flow buffer that already talks OLE DB, flat files, and Excel is why finance shops still ship month-end in a .dtsx. Fuzzy Lookup, Slowly Changing Dimension, and a SQL Agent job on the same box are a real stack if the destination is SQL Server. Lift-and-shift to Azure-SSIS IR keeps those packages. If the job is "run the package we wrote in 2016," that is a real product.

Where the job is wider

SSIS is a package runner. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The package ends; then you query SQL Server.

CDC is the ops. SQL Server 2025 does not ship Attunity CDC. Azure-SSIS Enterprise still does, and you pay the Enterprise node-hour plus the catalog database. An always-on IR bills every hour you left it up. Hybrid Benefit and a start/stop window change that bill; they do not change the model.

Fabric Invoke SSIS Package is Preview and narrower than the blog: OneLake only, no on-prem, no VNet, no third-party components. Hadoop tasks are gone. Oracle connector is gone on 2025.

Skippr will not run a .dtsx. Skippr also will not host SSISDB. If the job is "keep the Visual Studio package," SSIS is the narrower tool. If the job is live SQL on Iceberg you run, SSIS 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 SSIS if you want to keep Visual Studio packages on SSISDB, and a SQL Server after the package is enough.

Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not run a .dtsx.

Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.