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Adeptia alternative — Adeptia Automate vs Skippr ​

Adeptia Automate is the current name for Adeptia Connect. It is first-mile onboarding: partner files, EDI-shaped payloads, and unstructured inbound that have to become a system-of-record row. AI mapping and IDP sit on that path. You publish a flow as a REST API. Pricing is Workloads (processing engines), not seats or connectors. Plans are quote: Basic is Adeptia-hosted with the connector library and mapping copilot (SSO and custom forms are add-ons). Professional adds on-prem or private cloud, VPN / VNET, SSO, RBAC, and custom forms. Enterprise adds multi-zone HA, version control, and tenant boundaries. Extra Workloads buy concurrency. Incremental load, where it exists, is cursor or timestamp style, not a log reader. Object storage writers cover S3 and peers with Parquet or Avro. Iceberg is not a destination. This is partner integration, 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 Workload meter. ELT into a warehouse is one path, not the product.

FeatureSkipprAdeptia Automate
DeploySingle Rust binary, your VPC or on-premAdeptia-hosted on Basic. On-prem / private cloud on Professional
SchemaIn-stream evolution, sibling columnsMapping copilot plus IDP on inbound files
StorageOpen Iceberg on Parquet you ownSystem of record or Parquet/Avro on object storage. No Iceberg
CDCLog-based; fails closed if the dest cannot honourIncremental by key or timestamp. Not log CDC
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineTarget after the flow. No lake SQL in Adeptia
PricingFree self-hosted engineQuote per Workload. Basic / Professional / Enterprise.

What Adeptia is good at ​

A supplier file that is never quite the same twice. That is the product. Business users map with the copilot, IT publishes the API, the partner keeps sending. On-prem Professional is why regulated shops still buy it instead of a SaaS iPaaS. If the job is "first-mile partner onboarding," that is a real product.

Where the job is wider ​

Adeptia is an onboarding engine. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The flow ends; then you query the system of record.

Workloads are the ops. Capacity is what you licensed, not rows. Basic will not sit in your VPC. FAQ copy still mentions transaction volume and connections in places; the pricing page now says Workloads. Ask which meter is on the order form.

Skippr will not ingest EDI or run IDP on a PDF. Skippr also will not be your partner portal. If the job is "map the inbound file," Adeptia is the narrower tool. If the job is live SQL on Iceberg you run, Adeptia 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
bash
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")
bash
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 Adeptia Automate if you need first-mile partner files into a system of record, and a flow after mapping is enough.

Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not parse EDI.

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