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Nexla alternative — Nexla vs Skippr ​

Nexla is a data-product fabric. A connector (they claim 1,000-plus) lands in a Nexset: schema, metadata, validation, lineage, and access on one virtual product. The same Nexset can feed an API, a warehouse load, a Kafka topic, or an MCP agent without a second pipeline. CDC sources on the product page include Postgres, MySQL, Oracle, SQL Server, and MongoDB. Streams include Kafka, Kinesis, Pub/Sub, JMS, and Event Hubs. Iceberg is a named destination: Glue, S3 Tables, Hive Metastore, and REST catalog today; Unity, Polaris, and Nessie listed as coming. They sell snapshot loads at 1.2 to 3-plus GB/s and a streaming CDC sink with upsert and schema evolution. Official pricing is usage credits. AWS Marketplace Team is 10,000 USD for 12 months and 10,000 credits; extra usage is 1 USD a unit. Enterprise is a private offer. This is not a Streamhouse. There is no SQL engine in Nexla.

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

FeatureSkipprNexla
DeploySingle Rust binary, your VPC or on-premNexla cloud. Enterprise can be private
SchemaIn-stream evolution, sibling columnsNexset schema plus catalog annotations
StorageOpen Iceberg on Parquet you ownNexset, then warehouse, Kafka, or Iceberg
CDCLog-based; fails closed if the dest cannot honourLog CDC into a Nexset. Iceberg sink is documented
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineConsumers of the Nexset. No lake SQL in Nexla
PricingFree self-hosted engineCredits. Marketplace Team 10,000 USD / 10,000 credits / year.

What Nexla is good at ​

One Nexset, many speeds. That is the product. A CDC feed that an agent reads over MCP and a warehouse reads in batch without two graphs is the pitch. Iceberg writes without standing up Spark is why lakehouse teams look here. If the job is "govern this feed as a product," that is a real product.

Where the job is wider ​

Nexla is a Nexset plane. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Iceberg is a sink the Nexset writes. You still query Athena or Trino after.

Credits are the ops. Team is 10,000 units a year on the Marketplace card. Overage is 1 USD a unit. What a credit is on a given CDC job is a Nexla conversation, not a public formula. Unity and Polaris catalogs are still "coming soon" on the Iceberg page.

Skippr will not mint a Nexset or an MCP product. Skippr also will not match a 1,000-connector catalog. If the job is "virtual data product to many consumers," Nexla is the narrower tool. If the job is live SQL on Iceberg you run, Nexla 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 Nexla if you want a Nexset that many consumers read, and Iceberg as a sink is enough.

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

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