OLake alternative — OLake vs Skippr

OLake Go is Apache-2.0 extract-load from databases, Kafka, and S3 into Apache Iceberg or plain Parquet. Datazip builds it. You install a Docker Compose UI (Temporal plus a metadata Postgres) or Helm. Full load plus log CDC covers Postgres (pgoutput), MySQL, MongoDB, and SQL Server. Oracle and DB2 are full refresh and incremental; Oracle CDC is still marked WIP. Kafka is latest-offset bounded incremental, not a continuous consumer. Jobs run on a schedule you set: every minute, hourly, daily, weekly, or a cron. Their own compare page calls that scheduled copies: CDC captures recent changes, writes them, then stops. Compaction lives in a second product, OLake Fusion, also Apache-2.0.

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 Temporal stack. ELT into a warehouse is one path, not the product.

Feature	Skippr	OLake
Deploy	Single Rust binary, your VPC or on-prem	Docker Compose UI (port 8000) or Helm. Temporal plus metadata Postgres
Schema	In-stream evolution, sibling columns	Widening type changes. DLQ columns listed as coming soon
Storage	Open Iceberg on Parquet you own	Iceberg or Parquet. Glue, Hive, JDBC, REST (Nessie, Polaris, Unity, Lakekeeper)
CDC	Log-based; fails closed if the dest cannot honour	Full plus CDC on Postgres, MySQL, MongoDB, SQL Server. Job stops after the run
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	Your query engine after the write. No built-in SQL
Pricing	Free self-hosted engine	Apache-2.0, pay your infra. Fusion is a second install. See their terms for SaaS.

What OLake is good at

Database to Iceberg without standing up Kafka, Debezium, Spark, or Flink. Parallel chunking on snapshots. Stateful resume from a checkpoint. Two-phase commit on Iceberg writes (olake_2pc) so a crashed chunk does not double-write. Upsert can emit equality deletes, positional deletes, or Iceberg v3 deletion vectors, picked for the query engine you named. That is a real EL product if the job is "copy these tables into Iceberg I already query elsewhere."

Fusion is the honest companion: Lite / Medium / Full compaction on a cron, one target-size knob instead of Spark's rewrite_data_files list. If table maintenance is the pain, that is the tool they shipped for it.

Where the job is wider

OLake is extract-load on a schedule. Freshness is the cron you chose. Their terminology page is explicit: a CDC run captures, replicates, then stops. Airflow or the built-in timer is how you get near-real-time. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Transforms sit downstream.

Kafka as a source is bounded incremental to the latest offset. It is not a standing consumer. Oracle CDC is WIP; production Oracle is full plus incremental JDBC. Athena does not support deletion vectors, so check the engine before you pick that delete format.

The UI is a compose stack, not one binary: default login is admin / password on localhost:8000. Fusion is a second deploy if you want compaction in the same house. Skippr will not give you that UI, Temporal, or Fusion's tiered rewrite. Skippr also does not have Oracle or DB2 sources today. If the job is "scheduled Iceberg copies from those databases, plus a compaction UI," OLake is the narrower tool. If the job is live SQL on Iceberg you run, OLake 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 OLake if you want scheduled database-to-Iceberg copies, their UI, and Fusion for compaction, and a query engine after the write is enough.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not ingest Oracle or DB2 yet.

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

OLake alternative — OLake vs Skippr ​

What OLake is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

OLake alternative — OLake vs Skippr

What OLake is good at

Where the job is wider

Getting started

Which to choose