Amazon Athena alternative — Amazon Athena vs Skippr

Amazon Athena is serverless SQL (and Spark) on S3. You point a workgroup at Glue (or another catalog) and pay for data scanned or for DPUs. SQL is 5 USD per TB scanned (10 MB minimum). Provisioned capacity is 0.30 USD per DPU-hour. Spark is 0.35 USD per DPU-hour. S3 storage, requests, and Glue catalog are extra. Glue Data Catalog: first million metadata objects free, then 1 USD per 100,000 per month. Iceberg compaction in Glue is 0.44 USD per DPU-hour (per-second, 1-minute minimum). Athena reads Iceberg v2 (positional and equality deletes). It does not read Iceberg v3. CDC is not Athena; DMS, Glue, or skipprd land the table, then Athena queries it. skipprd's AthenaIceberg dest is how Skippr registers the same Glue catalog Athena already sees. 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. Athena still works on committed snapshots. ELT into a warehouse is one path, not the product.

Feature	Skippr	Amazon Athena
Deploy	Single Rust binary, your VPC or on-prem	Serverless workgroup. No cluster
Schema	In-stream evolution, sibling columns	Glue / Iceberg catalog. v3 tables are unread
Storage	Open Iceberg on Parquet you own	S3 you already have. Athena does not store rows
CDC	Log-based; fails closed if the dest cannot honour	Not a CDC product. Query after something else lands
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Athena SQL on committed Iceberg v2 / Hive / Parquet
Pricing	Free self-hosted engine	5 USD/TB scanned, or DPU-hours. Glue and S3 extra.

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What Amazon Athena is good at

SQL on S3 without a cluster, billed per scan, on tables Glue already knows. That is the product. Iceberg v2 in Glue is why some lakes skip a warehouse for ad-hoc SQL. If the job is "this SELECT on this bucket," Athena is a real product.

Where the job is wider

Athena is a query engine. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Iceberg v3 is unread. CDC still starts in DMS, Glue, or skipprd. Skippr will not replace Athena for serverless SQL over committed files.

Workgroups, Glue, and scanned TB are the ops. Skippr will not run Athena. Glue, DMS, and Firehose are separate pages.

If the job is "Athena we already staff," Athena is the narrower tool. If the job is live SQL on Iceberg you run, the workgroup 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

CLI

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")

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 Athena if you want serverless SQL on S3, and Iceberg v2 in Glue is enough.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run Athena. Athena can still query the Iceberg Skippr commits.

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