SAS Data Integration alternative — SAS Data Integration vs Skippr
SAS Data Integration Studio is the SAS 9 job. You draw a DIS flow, the workspace or grid runs it, metadata lives in the SAS 9 repository. The Viya path is SAS Data Engineering, used from SAS Data and AI Studio (SAS Studio renamed in the June 2026 Viya release; licensing did not change with the name). Studio Engineer is the advanced flow designer. Studio Analyst is the lighter one. Compute is SAS Compute, CAS (in-memory), or a container. Cloud Data Exchange and a remote Data Agent pull hybrid sources. Job Flow Scheduler chains them. Workload Management sits on Kubernetes. Iceberg arrived in Viya 2026.01 through SAS/ACCESS to DuckDB, including S3 Table Buckets. A SAS blog of that release says schema evolution is not supported on that path. SAS prices by user, core capacity, transactions, or revenue depending on the offering. Public FAQ floors are 1,500 USD a user or 10,000 USD of core capacity for selected products, then quote. This is an analytics platform's ETL, not a Streamhouse.
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 DIS repository. ELT into a warehouse is one path, not the product.
| Feature | Skippr | SAS Data Integration |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | SAS 9 DIS, or Viya Data Engineering in Data and AI Studio |
| Schema | In-stream evolution, sibling columns | DIS metadata or Viya flows. Iceberg via DuckDB has no schema evolution |
| Storage | Open Iceberg on Parquet you own | SAS data sets, CAS, warehouse, or Iceberg through DuckDB |
| CDC | Log-based; fails closed if the dest cannot honour | Scheduled or event flows. Not a log CDC product |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | SAS, CAS, or the warehouse after the job |
| Pricing | Free self-hosted engine | Quote (users, cores, or other SAS metrics). |
What SAS Data Integration is good at
A SAS job that already talks to the rest of the SAS estate. That is the product. Quality definitions, Visual Analytics, and a DIS flow a bank has run for a decade are why migrations go to Studio Engineer instead of a greenfield ELT tool. If the job is "keep the SAS flow, run it on Viya," that is a real product.
Where the job is wider
DIS or Studio Engineer is a job designer. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight CDC. Iceberg in 2026.01 is a DuckDB LIBNAME, not a CDC sink, and schema evolution is off.
License math is the ops. Users, cores, transactions, or revenue: the FAQ lists all of them. A UK reseller card for Data Engineering exists; SAS's own site still says talk to an account team.
Skippr will not import a DIS job. Skippr also will not run CAS. If the job is "SAS 9 to Viya data engineering," SAS is the narrower tool. If the job is live SQL on Iceberg you run, SAS DI 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 SAS Data Integration if you already run DIS or Viya Data Engineering, and SAS after the job is enough.
Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not import a DIS job.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.