Apache Hop alternative — Apache Hop vs Skippr

Apache Hop is a visual ETL workbench. Matt Casters and the old Kettle team rewrote it from scratch. It is not a Pentaho release. Pipelines and workflows live as JSON in a folder, so Git is the repo. The GUI (and hop-run) execute more than 250 transforms and 80 workflow actions across 40-plus database dialects. The same pipeline can run locally, on Hop Server, on native Spark, or on Flink and Dataflow through Apache Beam. Release 2.19.0 (17 Aug 2026) wants Java 21. Iceberg and Delta are Lake Table transforms: native Spark (Spark 4.1, Iceberg 1.11.0) does merge, compaction, vacuum; the Hop engine can read and write Iceberg only; Beam engines are out of scope. Hop Web is still marked work-in-progress, as-is. You can import many Pentaho .ktr / .kjb files. The project is Apache-2.0. Commercial support is third parties (know.bi, Avizing, Yupiik), not an Apache SKU.

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 canvas. No Java 21 workbench. ELT into a warehouse is one path, not the product.

Feature	Skippr	Apache Hop
Deploy	Single Rust binary, your VPC or on-prem	Java 21 GUI, Hop Server, Spark, or Beam. Docker and Helm exist
Schema	In-stream evolution, sibling columns	Per-transform metadata. You wire the mapping
Storage	Open Iceberg on Parquet you own	JDBC, files, and Lake Tables (Iceberg / Delta) on the engines that support them
CDC	Log-based; fails closed if the dest cannot honour	Database and file incremental transforms. Not a log CDC product
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Destination after the pipeline. Time travel is a Lake Table input option
Pricing	Free self-hosted engine	Apache-2.0, pay the JVM or Spark. Support is third-party.

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What Apache Hop is good at

The canvas is honest for row transforms people used to draw in Spoon. JSON on disk is a real Git story, unlike a PDI repository database. One click import of a lot of old .ktr / .kjb is why Hitachi shops look here when PDI stalls. Environments swap credentials without rewriting the graph. If the job is "visual hops, 250 transforms, run it on Spark when it gets big," that is a real workbench.

Where the job is wider

Hop is visual ETL. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Lakehouse I/O is engine-split: Spark for merge and maintenance, Hop engine for Iceberg read/write, Beam not at all. Pick the run configuration wrong and the Lake Table transform does not exist.

Hop Web is as-is. Docs for writing plugins from scratch are thin. CDC is not the centre; you will not get skipprd's fail-closed log path from a hop graph.

Skippr will not give you the GUI, 250 transforms, or a .ktr importer. If the job is "draw the pipeline the Kettle way," Hop is the narrower tool. If the job is live SQL on Iceberg you run, Hop 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 Apache Hop if you want a Kettle-shaped canvas, JSON in Git, and a warehouse or Lake Table after the run is enough.

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

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