BladePipe alternative — BladePipe vs Skippr

BladePipe is a CDC engine with a console and a worker. Community is free (five DataJobs, 500 tables each). Cloud is managed or BYOC: incremental CDC is 10 USD a million rows, full load 0.01 USD a million, verification 0.01, correction 0.10, schema migration 0.10 USD per 100 tables. Enterprise on-prem is a prepaid DataJob licence (144 USD a month for one job, 720 for five). Latency is published under 5 seconds. Iceberg is a target: Glue plus S3, Nessie plus MinIO or S3, REST catalog plus MinIO or S3. Snapshot then log CDC, DDL sync, verification, and correction sit in the same product. This is not a Streamhouse. There is no Iceberg ∪ WAL.

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 per-million-row meter. ELT into a warehouse is one path, not the product.

Feature	Skippr	BladePipe
Deploy	Single Rust binary, your VPC or on-prem	Community worker, Cloud managed/BYOC, or Enterprise on-prem
Schema	In-stream evolution, sibling columns	Schema migration plus DDL sync
Storage	Open Iceberg on Parquet you own	Iceberg on Glue, Nessie, or REST, plus warehouse dests
CDC	Log-based; fails closed if the dest cannot honour	Snapshot then incremental. Verification and correction are extra meters
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	After the Iceberg writer. Compaction is still yours
Pricing	Free self-hosted engine	Cloud: 10 USD/M incremental rows. Enterprise: DataJob licence.

What BladePipe is good at

A MySQL or SQL Server log that needs to land in Iceberg without Kafka, Flink, and Debezium as three moving parts. That is the product. Built-in verify/correct is why some teams pick it over a Connect sink. If the job is "this database into this Iceberg catalog," that is a real product.

Where the job is wider

BladePipe is a CDC replicator. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Iceberg here is a dest connector. Small files and catalog ops stay the lake's.

Row math is the ops. Incremental at 10 USD a million is the line that grows. Full load is cheap; CDC is not. Skippr will not run BladePipe verification. Skippr also will not replace a DataJob that already writes Glue Iceberg.

If the job is "BladePipe CDC we already staff," BladePipe is the narrower tool. If the job is live SQL on Iceberg you run, BladePipe 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 BladePipe if you want CDC into Iceberg billed per million rows, and the catalog after apply is enough.

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

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

BladePipe alternative — BladePipe vs Skippr ​

What BladePipe is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

BladePipe alternative — BladePipe vs Skippr

What BladePipe is good at

Where the job is wider

Getting started

Which to choose