AWS DMS alternative — AWS DMS vs Skippr

AWS Database Migration Service is managed log CDC. A task does a full load, then keeps applying changes, or applies changes only. It reads Oracle redo (LogMiner or Binary Reader), SQL Server MS-Replication or MS-CDC, MySQL row-based binlog, and PostgreSQL WAL. Targets include RDS engines, Redshift, DynamoDB, S3, Kinesis, MSK, OpenSearch, DocumentDB, Neptune, and Kafka. Iceberg is not a target. S3 writes CSV by default or Parquet. CDC files are timestamped; the first field on each row is I, U, or D. There is no ALTER on an S3 target when a source column changes. BatchApply is not supported on S3 and can lose data. Full LOB mode is not supported on S3. AWS publishes no SLA for CDC latency. You pay a replication instance (dms.t3.medium is 0.0745 USD an hour Single-AZ on-demand in us-east-1) or Serverless DCUs (2 GB RAM each): 0.0819 USD per DCU-hour Single-AZ, 0.1639 Multi-AZ, from 1 to 384 DCU. Transfer into DMS is free; so is transfer to RDS or EC2 in the same AZ. 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 DCU. ELT into a warehouse is one path, not the product.

Feature	Skippr	AWS DMS
Deploy	Single Rust binary, your VPC or on-prem	Replication instance or Serverless in an AWS account
Schema	In-stream evolution, sibling columns	Task mapping. S3 dest does not ALTER on column change
Storage	Open Iceberg on Parquet you own	Engine dest, or S3 CSV/Parquet. Iceberg needs Glue after
CDC	Log-based; fails closed if the dest cannot honour	Log CDC on Oracle, SQL Server, MySQL, Postgres. No latency SLA
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Query the target after the task. No lake SQL in DMS
Pricing	Free self-hosted engine	Instance-hours or DCU-hours. Serverless 0.0819 USD/DCU-hour Single-AZ.

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What AWS DMS is good at

A cutover that already copies Oracle to RDS and then tails redo until you flip. That is the product. Homogeneous and heterogeneous engine pairs on one task are why some migrations stay in AWS. If the job is "move this database into this account," that is a real product.

Where the job is wider

DMS is a replication service. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. S3 is files. Iceberg is a Glue job you write next.

Instance math is the ops. Serverless still bills every DCU the task holds. A CDC task that runs for months is an always-on line, not a row tax. Skippr will not capture VSAM, IMS, or every Oracle quirk DMS lists. Skippr also will not replace a DMS task that already lands in RDS.

If the job is "AWS database move we already staff," DMS is the narrower tool. If the job is live SQL on Iceberg you run, DMS 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 AWS DMS if you need a managed database move into AWS engines or S3 files, and a warehouse after the task is enough.

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

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