Astera alternative — Astera Centerprise vs Skippr

Astera Centerprise (also sold as Centerprise AI) is a no-code ETL studio. You draw a flow, or an agent drafts one from a prompt, and a person reviews it before the Centerprise server runs it. Connectors are 60-plus native plus 200-plus custom API. Ingestion is full load, incremental, or what Astera calls trigger-based CDC: the same pipeline does the first load and later changed rows. That is not a redo or WAL reader. Transforms are 200-plus in-engine or pushdown on Snowflake, SQL Server, or Postgres. Warehouse automation will generate star, snowflake, or Data Vault 2.0 loads, including SCD 1, 2, 3, and 6. EDI (X12, EDIFACT, HL7) and OCR sit on the same canvas. Destinations in the knowledge hub are warehouses and files (Parquet, Avro, JSON). Iceberg is not listed. Price is a custom subscription: deployment, users, and scale, fixed up front. On-prem, private cloud, or hybrid.

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 designer seat. ELT into a warehouse is one path, not the product.

Feature	Skippr	Astera Centerprise
Deploy	Single Rust binary, your VPC or on-prem	Centerprise server on-prem, private cloud, or hybrid
Schema	In-stream evolution, sibling columns	Visual mapper. Model-driven regen when the model changes
Storage	Open Iceberg on Parquet you own	Warehouse or files. No Iceberg destination listed
CDC	Log-based; fails closed if the dest cannot honour	Trigger-based incremental. Same pipeline for full then delta
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Target after the job. No lake SQL in Centerprise
Pricing	Free self-hosted engine	Quote. Fixed up-front subscription.

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What Astera Centerprise is good at

A dimensional load an analyst can see. That is the product. Data Vault or SCD generated from an approved model is why warehouse teams still buy it instead of SSIS plus a second modeller. EDI and PDF extraction on the same canvas is a real adjacent job. If the job is "design the star, generate the pipelines," that is a real product.

Where the job is wider

Centerprise is a pipeline studio. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. The job ends; then you query Snowflake.

CDC is trigger or incremental. Astera's own copy says trigger-based. Deletes that never hit a trigger do not arrive. AI drafts at design time; approved jobs run without the model in the path. That is honest. It still is not log CDC.

Skippr will not generate a Data Vault or parse X12. Skippr also will not host a Centerprise server. If the job is "no-code warehouse automation," Centerprise is the narrower tool. If the job is live SQL on Iceberg you run, Centerprise 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 Astera Centerprise if you want no-code warehouse automation and EDI on one canvas, and a warehouse after the job is enough.

Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not generate a Data Vault.

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