BigQuery alternative — BigQuery vs Skippr

BigQuery is Google Cloud's warehouse. SQL runs on slots or on-demand bytes. Iceberg managed tables (formerly BigLake tables for Apache Iceberg in BigQuery) store Parquet in a bucket you own and keep a managed experience. Storage is Cloud Storage; there is no extra BigQuery storage SKU. Automatic table management (compaction, clustering, garbage collection, metadata refresh) bills 0.12 USD per DCU-hour (list, region-dependent). On-demand queries: first 1 TiB per month free, then 6.25 USD per TiB. CDC into Iceberg managed tables uses BigQuery CDC apply; out-of-order deletes are discarded (no delete-retention window). Datastream and DTS are separate pages. This is not Iceberg ∪ WAL you query in one binary.

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 slot reservation for a sync. ELT into a warehouse is one path, not the product.

Feature	Skippr	BigQuery
Deploy	Single Rust binary, your VPC or on-prem	Google Cloud project. Slots or on-demand
Schema	In-stream evolution, sibling columns	Table DDL. Iceberg schema in the managed catalog
Storage	Open Iceberg on Parquet you own	Native tables, or Iceberg in your GCS bucket
CDC	Log-based; fails closed if the dest cannot honour	Datastream, DTS, or BQ CDC apply on managed Iceberg
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	BigQuery SQL after the table exists
Pricing	Free self-hosted engine	On-demand from 6.25 USD/TiB after 1 TiB free.

What BigQuery is good at

Serverless warehouse SQL on Google Cloud, including Iceberg files in your bucket with managed compaction. That is the product. Slots, editions, and the Storage Write API are why some lakes stay in BigQuery even when the format is open. If the job is "this BigQuery project, and Iceberg as a managed table," that is a real product.

Where the job is wider

BigQuery is a warehouse. It is not a Streamhouse. There is no Iceberg ∪ WAL you query in one binary on your machine. There is no skipprd query on in-flight lake rows without BigQuery running. CDC still starts in Datastream, DTS, or skipprd; Iceberg managed CDC drops out-of-order deletes. Skippr will not replace BigQuery slots or the Google Cloud bill.

Slots (or scanned TiB) and DCU-hours are the ops. Skippr will not run BigQuery SQL. Datastream and DTS are separate pages.

If the job is "BigQuery we already staff," BigQuery is the narrower tool. If the job is live SQL on Iceberg you run, the warehouse 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 BigQuery if you want a Google Cloud warehouse, and Iceberg as a managed table is enough.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run BigQuery.

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

BigQuery alternative — BigQuery vs Skippr ​

What BigQuery is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

BigQuery alternative — BigQuery vs Skippr

What BigQuery is good at

Where the job is wider

Getting started

Which to choose