Azure Event Hubs alternative — Azure Event Hubs vs Skippr

Azure Event Hubs is Microsoft's managed ingest log. Namespaces hold event hubs. Kafka clients (protocol 1.0+) talk to Standard, Premium, or Dedicated; Microsoft says full Kafka protocol compatibility needs Premium or Dedicated. Schema Registry sits on those same tiers. Iceberg is not in Event Hubs. Capture copies the stream into your Blob Storage or ADLS Gen2 account as Apache Avro (EventData with Body as bytes). A portal no-code path can land Parquet via Stream Analytics. Neither path writes Iceberg metadata. CDC is an upstream writer into the hub, not Event Hubs itself. Tiers: Basic (1 day, no Kafka, no Capture), Standard (7 days, Kafka, Capture billed extra), Premium (90 days, Capture included, processing units), Dedicated (capacity units, 4-hour minimum). One Standard throughput unit is 1 MB/s ingress and 2 MB/s egress, 84 GB retention. East US list is about 0.03 USD per TU-hour and 0.028 USD per million ingress events; Capture about 0.10 USD per TU-hour (about 73 USD per TU-month) plus the storage account. Premium is about 1.03 USD per PU-hour; Dedicated about 6.85 USD per CU-hour. Geo-replication data transfer on Premium/Dedicated is 0.09 USD/GB in North America and Europe. 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. No Event Hubs namespace. ELT into a warehouse is one path, not the product.

Feature	Skippr	Azure Event Hubs
Deploy	Single Rust binary, your VPC or on-prem	Azure-managed namespace (Basic / Standard / Premium / Dedicated)
Schema	In-stream evolution, sibling columns	Optional Schema Registry on Standard and above
Storage	Open Iceberg on Parquet you own	The log. Capture is Avro (or Parquet via Stream Analytics)
CDC	Log-based; fails closed if the dest cannot honour	An upstream writer. Event Hubs is the bus
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Consumers on the hub. Lake after Capture or a Spark job
Pricing	Free self-hosted engine	East US: about 0.03 USD/TU-hour + 0.028 USD/million events. Capture extra on Standard.

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What Azure Event Hubs is good at

A managed log that Kafka clients can reuse, with Capture so you do not have to run a consumer just to land Avro in your account. That is the product. Premium isolation and Dedicated clusters are why some Azure estates never stood brokers. If the job is "this event onto this hub," that is a real product.

Where the job is wider

Event Hubs is a message bus. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight lake rows. Capture Avro is an event envelope; Iceberg is a later Spark or Fabric job. skipprd's Kafka source speaks PLAINTEXT / SASL_PLAINTEXT only, so Event Hubs' TLS Kafka endpoint is not a Skippr source today. Postgres CDC still starts before the hub.

TUs, PUs, or CUs, Capture windows, and the storage account are the ops. Skippr will not replace an Event Hubs namespace that already fans out to Stream Analytics, Functions, and Spark. Skippr also will not run Event Hubs. Fabric and Azure Data Factory are separate pages.

If the job is "Event Hubs we already staff," Event Hubs is the narrower tool. If the job is live SQL on Iceberg you run, the hub 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 Event Hubs if you want a managed Azure log, and Avro Capture or a later Spark Iceberg job is enough.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not run Event Hubs.

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