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Quix alternative — Quix vs Skippr ​

Quix is Python stream processing. Quix Streams is the library. Quix Cloud deploys those apps (BYO Kafka, or theirs). Quix Lake persists Kafka topics into your object storage (S3, Azure Blob, GCS, MinIO, or a Bridge folder). Two lake options: Data Lake writes raw Kafka messages as Avro segments plus a Parquet index for replay; Lakehouse writes Hive-style key=value/ Parquet folders plus a Quix-managed catalog for SQL. Docs are explicit: Lakehouse is not Iceberg. Spark, Trino, and DuckDB can read the Hive layout; they cannot open it as an Iceberg table. Iceberg is a separate AWS S3 Iceberg Sink connector: Kafka topic to Glue-catalogued Iceberg on S3. CDC is a Kafka payload. AWS Marketplace lists Quix Cloud Enterprise at 3363.84 USD/month for a 2-node cluster with Basic support (136 GiB ingress/egress/Kafka/storage per node in that listing). Time-limited free Cloud accounts are booked with them. 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 Quix Cloud. ELT into a warehouse is one path, not the product.

FeatureSkipprQuix
DeploySingle Rust binary, your VPC or on-premQuix Streams locally, or Quix Cloud
SchemaIn-stream evolution, sibling columnsTopic schemas. Lakehouse catalog is Quix-managed
StorageOpen Iceberg on Parquet you ownHive Parquet (Lakehouse) or Avro+index (Data Lake). Iceberg is a Glue sink
CDCLog-based; fails closed if the dest cannot honourA Kafka topic. Not Postgres WAL
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineQuix Lake SQL on Hive Parquet. Iceberg after the S3 sink
PricingFree self-hosted engineStreams is a library. Cloud Enterprise Marketplace 3363.84 USD/month (2 nodes).

What Quix is good at ​

Python streaming DataFrames on Kafka, with Cloud to deploy them, and a lake in your bucket for replay or Hive SQL. That is the product. The S3 Iceberg Sink is why some teams still want Glue tables without standing Connect. If the job is "this Python app on this topic," that is a real product.

Where the job is wider ​

Quix is a stream app platform plus a Kafka persister. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Lakehouse will not emit Iceberg metadata. Postgres CDC still starts before Kafka.

Cloud nodes and two lake sinks (they do not share a consumer group) are the ops. Skippr will not replace a Quix Streams app that already transforms topics. Skippr also will not run Quix Cloud. Bytewax and Kafka Connect Iceberg are separate pages.

If the job is "Quix we already staff," Quix is the narrower tool. If the job is live SQL on Iceberg you run, Hive folders were 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 Quix if you want Python stream apps on Kafka, and Hive Parquet or a Glue Iceberg sink is enough.

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

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