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CloverDX alternative — CloverDX vs Skippr ​

CloverDX is a graph ETL platform. You draw (or code) a transformation in Designer. Server runs it: schedule, trigger, or a Data Service API. Ops Dashboard watches jobs. Version control is git. Marketplace adds connectors. You host it: on-prem, Docker, AWS, GCP, or Azure. There is no CloverDX SaaS. Licensing is DX Units. Each unit is either one Designer seat or one production Server CPU core. You reallocate. Data consumers are free. Data Manager, Wrangler, and Data Catalog are separate Business Tools seats. Plans are Standard, Plus, and Enhanced: same engine, more training, professional-services hours, non-prod servers, and 24/7 on Enhanced. Price is quote. Connectors are not a second meter. Docs for 7.6.1 list Parquet and Hadoop readers and writers. Iceberg is not in that catalog. CDC is a graph you build (incremental DB reads, native components they contrast with Boomi's extra SKU), not a log replicator you turn on.

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 DX Unit. ELT into a warehouse is one path, not the product.

FeatureSkipprCloverDX
DeploySingle Rust binary, your VPC or on-premDesigner plus Server you host. No vendor SaaS
SchemaIn-stream evolution, sibling columnsMetadata on the graph. Code when the visual component is not enough
StorageOpen Iceberg on Parquet you ownWriter you picked. Parquet yes. Iceberg not in 7.6.1
CDCLog-based; fails closed if the dest cannot honourIncremental graphs. Not a dedicated log CDC product
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineTarget after the job. No lake SQL in CloverDX
PricingFree self-hosted engineQuote per DX Unit (Designer seat or Server core).

What CloverDX is good at ​

A graph an engineer can debug with breakpoints. That is the product. Java when the palette stops, Data Services when the job should be an API, and a unit model that does not grow with rows. If the job is "complex ETL we will own on our metal," that is a real product.

Where the job is wider ​

CloverDX is a graph runner. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The graph ends; then you query the database.

DX Units are the ops. More cores or more Designers means more units. Business Tools are another seat line. Iceberg is not a writer in current docs. CDC is whatever incremental pattern you built.

Skippr will not run a Clover graph. Skippr also will not replace Data Manager. If the job is "engineered ETL on cores we license," CloverDX is the narrower tool. If the job is live SQL on Iceberg you run, CloverDX 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 CloverDX if you want Designer graphs on Server cores you host, and a database after the job is enough.

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

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