Alteryx alternative — Alteryx vs Skippr

Alteryx One is a prep canvas. You drag Blend, Join, and Aggregate in Designer Cloud or Designer Desktop, then share the workflow. Starter is the only public price: 250 USD a user a month, billed annually, cloud only, files only (csv, xlsx, json), no warehouse connectors, no automation runs. Professional is quote: desktop plus cloud, macros, geospatial, 100-plus sources (Snowflake, Databricks), and essential scheduling. Enterprise is quote: API-triggered runs, Server or a private data plane, row-level governance, App Builder, free viewer seats. Old Designer-only contracts map to Professional. Designer plus Server maps to Enterprise. Automation is a run-capacity meter on top of the seat. This is analyst ETL. It is not log CDC and it is not Iceberg.

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 Designer seat. ELT into a warehouse is one path, not the product.

Feature	Skippr	Alteryx
Deploy	Single Rust binary, your VPC or on-prem	Designer Cloud. Desktop and Server on Professional / Enterprise
Schema	In-stream evolution, sibling columns	Workflow tools. Output schema is what you built
Storage	Open Iceberg on Parquet you own	File or warehouse the workflow wrote. No Iceberg destination
CDC	Log-based; fails closed if the dest cannot honour	Scheduled or API-triggered workflow. Not log CDC
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Live Query on some editions. Not Iceberg ∪ WAL
Pricing	Free self-hosted engine	Starter 250 USD/user/month annual. Professional and Enterprise are quote.

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What Alteryx is good at

A join an analyst can see. That is the product. Geospatial macros and a Desktop canvas finance teams already staff are why month-end still happens here. Enterprise Server keeps the workflow off a laptop. If the job is "prep this extract before the model," that is a real product.

Where the job is wider

Alteryx is a workflow studio. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight CDC. The workflow ends; then you open the output.

Seat plus runs is the ops. Starter will not talk to Snowflake. Professional adds connectors and a thin schedule. Server and API triggers are Enterprise. A UK G-Cloud card lists Full Creator far above Starter; Alteryx's own site still says quote. Extra automation runs are a second line.

Skippr will not run a Designer workflow. Skippr also will not replace Auto Insights. If the job is "drag-and-drop prep on a seat," Alteryx is the narrower tool. If the job is live SQL on Iceberg you run, Alteryx 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 Alteryx if you want Designer prep on a seat, and a file or warehouse after the workflow is enough.

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

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