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Pentaho alternative — Pentaho vs Skippr ​

Pentaho Data Integration is Spoon. You draw transformations and jobs, the way Kettle always did. Hitachi Vantara shipped it for years. The pricing page is quote-only: Starter, Standard, Premium, Enterprise. The one public number is AWS Marketplace, from 3.57 USD an hour for the software on an m5.large, plus the instance. Community Edition is no longer a production door. Developer Edition 10.3 is Business Source License 1.1: Hitachi says not for production, aimed at students and universities, and it flips to Apache-2.0 four years after publish. Existing CE installs keep running. New business work is a 30-day trial, then a quote. Hop is the Apache rewrite if you want the canvas without that license.

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 Spoon. No core-count starter license. ELT into a warehouse is one path, not the product.

FeatureSkipprPentaho
DeploySingle Rust binary, your VPC or on-premSpoon plus Carte / server. AWS Marketplace image. Quote for production
SchemaIn-stream evolution, sibling columnsPer-step mapping in the transformation
StorageOpen Iceberg on Parquet you ownJDBC, files, and the enterprise connectors on the paid tier
CDCLog-based; fails closed if the dest cannot honourIncremental and CDC steps on licensed builds. Not the centre of Spoon
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineDestination after the job. BA is a separate license
PricingFree self-hosted engineQuote. AWS from 3.57 USD an hour. Developer Edition is BSL, not production.

What Pentaho is good at ​

The canvas taught a generation of warehouse teams to think in hops. A transformation is still a readable graph: table input, lookup, filter, table output. Carte and kitchen/pan are how you run that graph unattended. If you already have hundreds of .ktr / .kjb files and a Hitachi support contract, that is a real estate, not nostalgia.

Where the job is wider ​

Pentaho is visual ETL. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Business Analytics, Data Catalog, and Data Optimizer are separate SKUs.

The license is the operational fact now. BSL on Developer Edition is explicit: no production. Starter is limited cores and limited support. Dollar prices are not on pentaho.com. AWS's hourly meter is the only list you can read without a salesperson. If you want the canvas under Apache-2.0, that project is Hop, not a CE download.

Skippr will not import .ktr files or replace Spoon. If the job is "keep the Hitachi graph," Pentaho is the narrower tool. If the job is live SQL on Iceberg you run, Pentaho 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 Pentaho if you already run Spoon graphs under a commercial license and a warehouse after the job is enough.

Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not open .ktr files. For an Apache canvas, see Hop.

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