Amazon AppFlow alternative — Amazon AppFlow vs Skippr

Amazon AppFlow is a managed SaaS-to-AWS flow. You pick a source (Salesforce, SAP, ServiceNow, Marketo, Zendesk, and peers), a destination (S3, Redshift, Snowflake, EventBridge), and a schedule or an event. A successful flow run is 0.001 USD. A run that polls and finds nothing still bills. Data processed to an AWS-hosted or PrivateLink destination is 0.02 USD per GB. Destinations outside AWS and PrivateLink are 0.02 USD per GB for the first gigabyte and 0.10 USD per GB after that. SAP (and similar) connectors can add concurrent processes; each extra process is another flow run. S3 output is CSV, JSONL, or Parquet. Iceberg is not a destination. Salesforce Platform events and Salesforce CDC events are supported. There is no database log tail. No free tier. You also pay S3 requests and KMS for tokens and data in transit. 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 flow run. ELT into a warehouse is one path, not the product.

Feature	Skippr	Amazon AppFlow
Deploy	Single Rust binary, your VPC or on-prem	Flow in an AWS account
Schema	In-stream evolution, sibling columns	Flow field mapping
Storage	Open Iceberg on Parquet you own	S3 / Redshift / Snowflake. Parquet on S3, not Iceberg
CDC	Log-based; fails closed if the dest cannot honour	Salesforce events. Not a database log tail
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Athena or the warehouse after the flow
Pricing	Free self-hosted engine	0.001 USD per run plus 0.02 USD/GB on AWS dests.

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What Amazon AppFlow is good at

A Salesforce-to-S3 pull that already sits next to the rest of the AWS bill. That is the product. Event-triggered flows and PrivateLink connectors are why some teams use it instead of a second iPaaS. If the job is "this SaaS object into this bucket," that is a real product.

Where the job is wider

AppFlow is a SaaS mover. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Parquet on S3 is files. Iceberg is a Glue job you write next.

Run math is the ops. An empty poll still costs a run. Extra SAP concurrency multiplies the 0.001 USD line. Skippr will not match the SaaS connector catalog. Skippr also will not replace an AppFlow that already lands Salesforce in Redshift.

If the job is "SaaS into AWS we already staff," AppFlow is the narrower tool. If the job is live SQL on Iceberg you run, AppFlow 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 Amazon AppFlow if you want SaaS objects into S3 or Redshift, and Athena after the flow is enough.

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

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