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Bytewax alternative — Bytewax vs Skippr ​

Bytewax is Apache-2.0 stream processing: a Python dataflow API on a Rust runtime. Latest PyPI and GitHub release is 0.21.1 (25 Nov 2024). There has not been a 2025 or 2026 release. In May 2025 Bytewax the company stopped being commercially viable; the original core team stepped back. The project stays Apache-2.0 and community-maintained. Built-in connectors are Kafka (bytewax.connectors.kafka, pip install bytewax[kafka]), stdin/stdout, and files. You write the rest. There is no Iceberg source or sink. Kafka offsets for recovery live in Bytewax recovery partitions, not Kafka consumer groups; without recovery, workers start at the earliest offset unless you set starting_offset. CDC is a Kafka payload you parse (Debezium JSON, or anything you map). Deploy is python / Docker (bytewax/bytewax:0.21.1-python3.11) / your orchestrator. There is no current Bytewax Cloud. 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 dataflow workers. ELT into a warehouse is one path, not the product.

FeatureSkipprBytewax
DeploySingle Rust binary, your VPC or on-premPython process / Docker / K8s workers
SchemaIn-stream evolution, sibling columnsYour Python types. No Iceberg catalog
StorageOpen Iceberg on Parquet you ownWhatever sink you write. No Iceberg connector
CDCLog-based; fails closed if the dest cannot honourParse a Kafka topic yourself
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineNot a query engine. Downstream is yours
PricingFree self-hosted engineApache-2.0. No commercial Bytewax Cloud.

What Bytewax is good at ​

Stateful Python operators (windows, joins, recovery) without a JVM, on a Kafka topic you already have. That is the product. Custom connectors in the same language as the rest of the app is why some teams pick it over Flink SQL. If the job is "this Python dataflow," that is a real product.

Where the job is wider ​

Bytewax is a dataflow library. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query. There is no Iceberg sink. Postgres CDC is Debezium (or similar) in front. The company that sold support is gone; you staff the workers and the recovery store.

Recovery partitions and worker count are the ops. Skippr will not replace a Bytewax dataflow that already joins streams in Python. Skippr also will not run bytewax.run. Flink and Kafka Streams are separate pages.

If the job is "Python dataflows we already staff," Bytewax is the narrower tool. If the job is live SQL on Iceberg you run, the dataflow 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
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 Bytewax if you want a Python dataflow on Kafka, and you will write the lake sink yourself.

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

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