Keboola alternative — Keboola vs Skippr

Keboola is a SaaS data platform. Extractors land tables in Storage (a Snowflake, BigQuery, or DuckDB backend they run, or yours on a contract). SQL and Python transformations sit on those tables. Flow Builder chains the jobs. Writers push the result to a warehouse, a BI tool, or an app. Free is $0 with 120 compute minutes in month one, then 60 a month, then $0.14 a minute. Contracts switch the meter to Time Credits: a data-source hour costs 2 credits, a Small SQL hour costs 6, a destination write is 0.2 credits per GB out. Log-based CDC is a separate product, from $0.17 a minute or $1,300 a month, plus $700 a month per extra enterprise replication slot.

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 minute meter. ELT into a warehouse is one path, not the product.

Feature	Skippr	Keboola
Deploy	Single Rust binary, your VPC or on-prem	Multi-tenant SaaS. Enterprise can pick AWS, Azure, or GCP, or a private-cloud plane
Schema	In-stream evolution, sibling columns	Component mappings and Storage types. CDC adds new columns and ignores drops
Storage	Open Iceberg on Parquet you own	Table Storage on Snowflake, BigQuery, or DuckDB. File Storage on S3, Azure Blob, or GCS
CDC	Log-based; fails closed if the dest cannot honour	Debezium extractors. Self-serve MySQL, MariaDB, Postgres. Oracle, SQL Server, MongoDB on request
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	SQL workspace or Query Service against Storage, then the destination warehouse
Pricing	Free self-hosted engine	Free minutes then $0.14/min, or Time Credits on contract. See their pricing.

What Keboola is good at

One project instead of an ingest tool, a scheduler, and a transformation host. Hundreds of extractors, a Generic Extractor for REST APIs, and MIT-licensed component repos you can read. Flows give you conditions, retries, and cron without standing up Airflow. There is a data take-out button. For a small data team that wants a managed stack and will stay inside the free minutes, or will pay for a Time Credit contract, that is a real product.

Enterprise adds Data Catalog, Dev/Prod with Git, SSO, and a VPC or private-cloud plane. Storage can be your Snowflake or BigQuery (BYODB) instead of theirs.

Where the job is wider

Minutes and Time Credits are the bill. Extractors, SQL workspaces, Python jobs, and CDC each burn a different rate. On Free, unused minutes do not roll. Run out and jobs pause until you top up or the next monthly refill. Stay logged out for two months and they suspend the project, then delete the data. That is published, not a surprise.

Most of the platform is still batch. G2 reviewers who like the stack still say large pipeline changes get cumbersome, and that it can get pricey. CDC is the streaming exception, and it is a second product with its own rate. Query is Storage or the warehouse after the job. There is no Iceberg ∪ WAL. Components run as isolated Docker jobs they schedule. You do not run a binary.

Skippr will not give you Keboola's Flow Builder, Generic Extractor UI, or their catalog. If the job is "one SaaS project for extract, transform, and orchestrate," Keboola is the narrower tool. If the job is live SQL on Iceberg you run, Keboola 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.

YAMLCLIPython

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 Keboola if you want extract, transform, and orchestration in one SaaS project, and you are happy to pay minutes or Time Credits for that plane.

Choose Skippr if you want one binary, no compute-minute tax, live SQL, and Iceberg files that stay yours.

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

Keboola alternative — Keboola vs Skippr ​

What Keboola is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Keboola alternative — Keboola vs Skippr

What Keboola is good at

Where the job is wider

Getting started

Which to choose