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Striim alternative — Striim vs Skippr ​

Striim is a CDC and streaming platform. Cloud is consumption credits. Self-hosted is Striim Platform. A source adapter reads the log (PostgreSQLReader is standard; OracleReader, GGTrailReader, SalesforceCDCReader, and MSJet are premium). A target adapter writes the dest. Events count on the way in and on the way out. Cloud Enterprise monthly list (17 Sep 2026) is 100 credits per million standard events at the 1,000-credit floor, 200 for premium, then cheaper floors as volume rises. Mission Critical is 125 / 250 at its first floor. Credits also cover compute and transfer. IcebergWriter is a standard adapter. In current docs the only lake is GCS, via Dataproc Spark, with Lakehouse, Nessie, Polaris, or Hadoop catalog (Hadoop is for tests). AWS Marketplace Platform list is 19,200 USD a month for 8 cores, 38,400 for 16, 76,800 for 32, billed on cores not events. 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 credit per million events. ELT into a warehouse is one path, not the product.

FeatureSkipprStriim
DeploySingle Rust binary, your VPC or on-premStriim Cloud, or Platform on your VMs
SchemaIn-stream evolution, sibling columnsFlow TQL / adapters. Target mapping is per writer
StorageOpen Iceberg on Parquet you ownWarehouse writers, or IcebergWriter on GCS
CDCLog-based; fails closed if the dest cannot honourLog CDC readers. Oracle and some SaaS CDC are premium
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineAfter the target adapter. No lake SQL in Striim
PricingFree self-hosted engineCloud: credits per million events. Platform: core subscription.

What Striim is good at ​

An Oracle or SQL Server log that already needs to land in BigQuery, Databricks, Fabric, or Snowflake with a vendor who will staff the pipeline. That is the product. NonStop and MSJet readers are why some estates still pay premium credits. If the job is "this log into this warehouse, with Striim in the middle," that is a real product.

Where the job is wider ​

Striim is a streaming CDC runtime. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. IcebergWriter is GCS plus Dataproc in the current docs, not a lake you open with Trino on S3 after you leave GCS.

Credit math is the ops. In plus out is two event counts. Oracle is premium. A Platform core bill does not shrink when the pipeline is quiet. Skippr will not capture HP NonStop or MSJet. Skippr also will not replace a Striim Cloud that already writes BigQuery.

If the job is "Striim CDC we already staff," Striim is the narrower tool. If the job is live SQL on Iceberg you run, Striim 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 Striim if you want managed CDC streaming into a warehouse, and the dest after the writer is enough.

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

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