SAP Data Intelligence alternative — SAP Data Intelligence vs Skippr
SAP Data Intelligence is Kubernetes data orchestration. You build graphs: operators for extract, transform, ML, REST, and workflow. Replication flows sit on the Replication Management Service and pull ABAP-based systems. Metadata Explorer catalogs what those graphs touched. SAP Note 3407393 ends mainstream maintenance for the on-prem product on 31 December 2026. Note 3423165 ends the Cloud edition on 31 December 2028. SAP's successor write-up is not one product. Replication flows move to SAP Datasphere (same RMS component, a Premium Engagement migration tool exists). The catalog has a 1:1 story in Datasphere's Data Catalog. Pipelines do not: complex processing, machine learning, and REST graphs have no feature-parity map and get redesigned into Datasphere transformation flows, Integration Suite, Business Data Cloud, or something you already run. This is not Data Services and it is 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 graph. ELT into a warehouse is one path, not the product.
| Feature | Skippr | SAP Data Intelligence |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | Kubernetes cluster, cloud or on-prem |
| Schema | In-stream evolution, sibling columns | Operator ports plus dataset metadata |
| Storage | Open Iceberg on Parquet you own | Target you wired. No Iceberg ∪ WAL engine |
| CDC | Log-based; fails closed if the dest cannot honour | Replication flows (RMS) from ABAP systems |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | Target after the graph. No lake SQL in DI |
| Pricing | Free self-hosted engine | Quote. Maintenance ends 2026 on-prem, 2028 cloud. |
What SAP Data Intelligence is good at
Hybrid graphs that already talk SAP and a non-SAP lake in one canvas. Replication flows off S/4 without standing up a second CDC product are why some estates still run it. Metadata Explorer is a real catalog if that is what you bought. If the job is "keep the graph until 2026," that is a real (time-boxed) product.
Where the job is wider
Data Intelligence is an orchestrator. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The graph writes; then you query the target.
The successor is the ops. Replication is the only 1:1 path. Everything else is a redesign. Business Data Cloud (October 2026 TechEd) adds native Iceberg and a Polaris catalog on that newer stack, not on Data Intelligence. Capacity Units and Premium Outbound blocks (20 GB, first ten blocks at 500 CU) live on Datasphere / BDC, not on this SKU.
Skippr will not import a DI graph. Skippr also will not call RMS. If the job is "migrate DI replication to Datasphere," that is a Datasphere project. If the job is live SQL on Iceberg you run, Data Intelligence 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 SAP Data Intelligence only if you must keep an existing graph through the maintenance window.
Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not import a Data Intelligence pipeline.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.