Decodable alternative — Decodable vs Skippr
Decodable is managed Apache Flink with a Debezium-powered CDC library. Connections and pipelines are tasks. Small is 1 credit an hour, Medium 2, Large 4. Billing is per minute while a task is active. Idle is free. On Demand is 0.12 USD a credit. Enterprise is 0.10 USD a credit on annual capacity. Their Postgres-to-Snowflake illustration is one Medium source and one Medium sink: 4 credits an hour, 0.40 USD an hour on Enterprise. Kafka to Iceberg on S3 in the same table is 18 credits an hour, 1.80 USD. Iceberg sink is Glue catalog plus S3, Iceberg v2 default (v1 for old tables), Parquet/Avro/ORC, exactly-once. One connection writes one table. Change streams upsert; append streams append. An existing Iceberg table must match the stream schema or the sink fails with "table already exists with different schema." v1 tables cannot consume deletes. Redis announced it would acquire Decodable in Sep 2025 and fold it into Redis Cloud and RDI. The public site still sells Decodable as a product. 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 task credit. ELT into a warehouse is one path, not the product.
| Feature | Skippr | Decodable |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | Serverless Decodable, or BYOC data plane. Redis acquisition in progress |
| Schema | In-stream evolution, sibling columns | Flink SQL / Java / Python. Iceberg dest must match the stream |
| Storage | Open Iceberg on Parquet you own | Iceberg on Glue+S3, or warehouse sinks |
| CDC | Log-based; fails closed if the dest cannot honour | Managed Debezium. Snapshot plus log |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | After the sink. Preview is not lake SQL |
| Pricing | Free self-hosted engine | Credits while tasks run. 0.12 USD On Demand, 0.10 USD Enterprise. |
What Decodable is good at
A Postgres log that needs Flink SQL in front of Snowflake or one Iceberg table, without you running a Flink session cluster. That is the product. Per-minute idle-to-zero is why bursty jobs look cheaper than an always-on Connect worker. If the job is "managed Flink CDC we do not want to staff," that is a real product.
Where the job is wider
Decodable is a Flink control plane. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Iceberg here is Glue plus S3, one table per connection. Catalog mismatch fails the sink.
Credit math is the ops. Parallelism is extra Medium tasks. A SQL job with four tasks is 8 credits an hour before the sink. Redis now owns the roadmap; treat RDI integration as a second product, not this page. Skippr will not run Flink SQL. Skippr also will not replace a Decodable job that already upserts one Iceberg table.
If the job is "Decodable Flink we already staff," Decodable is the narrower tool. If the job is live SQL on Iceberg you run, Decodable 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.
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: ${APP_DB_PASSWORD}
database: app
tables: [orders, customers]
cdc_mode: snapshot_then_cdc
data_sinks:
lake:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1
schema_sink: schema_sinks.lake_schema
schema_sinks:
lake_schema:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1skipprd --workspace analytics --storage-mode local connect data-source postgres \
--pipeline orders \
--name app_db \
--host db.internal \
--user skippr_reader \
--password '${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_schemafrom 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")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 Decodable if you want managed Flink CDC billed on task credits, and the dest after the sink is enough.
Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not run a Decodable task.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.