Materialize alternative — Materialize vs Skippr

Materialize is an incremental streaming SQL database. You create a Postgres source on logical replication (Postgres 11+, no Kafka in the middle), then indexes and materialized views that stay fresh as the WAL moves. Iceberg is a public-preview sink: CREATE SINK ... INTO ICEBERG CATALOG, MODE UPSERT with a key, Parquet plus delete files, a snapshot at each COMMIT INTERVAL (docs recommend 1m to 5m in production). Catalogs on that path include AWS S3 Tables, Databricks Unity Catalog on AWS, Iceberg REST, and GCP BigLake in private preview. Append mode writes a changelog with _mz_diff and _mz_timestamp, not current state. Cloud On-Demand is 1.50 USD per compute credit in aws/us-east-1 and aws/us-west-2 (1.70 USD in aws/eu-west-1), plus storage and networking. Clusters bill per second. Self-managed is BSL 1.1, not Apache-2.0: Community needs a license key and caps one installation at 24 GiB memory and 48 GiB disk; Enterprise is a purchase. Version 20261007 converts to Apache-2.0 on 7 Oct 2030. This is not Iceberg ∪ WAL. Serving state lives in Materialize; Iceberg is a sink.

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 compute credit. ELT into a warehouse is one path, not the product.

Feature	Skippr	Materialize
Deploy	Single Rust binary, your VPC or on-prem	Materialize Cloud, or self-managed (K8s/Docker, BSL)
Schema	In-stream evolution, sibling columns	SQL DDL on sources and views
Storage	Open Iceberg on Parquet you own	Materialize state plus optional Iceberg sink (preview)
CDC	Log-based; fails closed if the dest cannot honour	Native Postgres logical replication. Kafka/Debezium optional
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	SQL on indexes and MVs. Iceberg is after the sink
Pricing	Free self-hosted engine	Cloud from 1.50 USD/credit (us-east-1). Community BSL 24 GiB / 48 GiB.

What Materialize is good at

Postgres CDC plus incrementally maintained SQL views you query in milliseconds, without Debezium and Kafka in the middle. That is the product. Strict serializable consistency and REPLICA IDENTITY FULL on the source are why some teams treat it as a live read replica. If the job is "streaming SQL we query now, Iceberg we land later," that is a real product. Iceberg preview is the honest landing path when you want S3 Tables or Unity after the view.

Where the job is wider

Materialize is a streaming database. It is not Iceberg ∪ WAL. Live SQL is on Materialize clusters. Iceberg is a sink in public preview, not the serving store. Upsert needs a key. Short commit intervals make small files. Append mode is a changelog, not a table you SELECT as current state.

Credit math is the ops on Cloud. Self-managed Community is a memory and disk cap plus a license key. Skippr will not be a Materialize-wire MV engine. Skippr also will not replace a Materialize cluster that already serves those views. RisingWave is a separate page.

If the job is "Materialize streaming SQL we already staff," Materialize is the narrower tool. If the job is live SQL on Iceberg you run, that is Skippr's job, not Materialize's Iceberg sink.

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 Materialize if you want incrementally maintained SQL views on Postgres CDC, and serving those views is the job.

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

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

Materialize alternative — Materialize vs Skippr ​

What Materialize is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Materialize alternative — Materialize vs Skippr

What Materialize is good at

Where the job is wider

Getting started

Which to choose