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Cloudera DataFlow alternative — Cloudera DataFlow vs Skippr ​

Cloudera DataFlow is managed Apache NiFi on Cloudera on cloud (AWS, Azure, GCP). You catalog, deploy, and monitor flows. Cloudera lists 450-plus connectors. Iceberg is a documented lake dest (PutIceberg / PutIcebergCDC). Functions are Stateless NiFi, billed per invocation, capped around 15 minutes, no canvas. Deployments and test sessions bill 0.30 USD a CCU-hour, per minute. Functions start at 0.10 USD a billable invocation, with volume discounts. A CCU is CPU plus memory; Cloudera says the printed rate is an estimate and varies by instance type. Your cloud VM, disk, and network are extra. Flow Management on Data Hub is the older NiFi SKU at 0.15 USD a CCU-hour. Lakehouse Optimizer (Iceberg maintenance) is 0.05 USD a CCU-hour. On-prem is Cloudera Flow Management / Base, sales quote. 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 CCU. ELT into a warehouse is one path, not the product.

FeatureSkipprCloudera DataFlow
DeploySingle Rust binary, your VPC or on-premNiFi deployments on EKS/AKS/GKE, or Functions. On-prem is Flow Management
SchemaIn-stream evolution, sibling columnsPer-processor. Record readers and writers. You wire the convert
StorageOpen Iceberg on Parquet you ownWherever the last processor writes. Iceberg is one sink
CDCLog-based; fails closed if the dest cannot honourCaptureChange / Debezium-shaped processors on supported databases
Queryskipprd query on Iceberg ∪ WAL; history in any Iceberg engineProvenance on FlowFiles. SQL is QueryRecord on a queue
PricingFree self-hosted engine0.30 USD/CCU-hr deployments. Functions from 0.10 USD/invocation. Cloud bill extra.

What Cloudera DataFlow is good at ​

A NiFi canvas you do not want to cluster yourself, with Iceberg as one of many dests and 450 connectors already on the palette. That is the product. Functions are the honest burst path when a flow is short. If the job is "route anything to anything on Cloudera's NiFi," that is a real product.

Where the job is wider ​

DataFlow is managed NiFi. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. QueryRecord runs on a queue. The lake starts after PutIceberg.

CCU math is the ops. A flow that sits idle still holds a pod. The 0.30 USD line is software only. Functions have no UI and a time cap. Skippr will not give you the canvas, provenance search, or that processor list. Bare NiFi is a separate page. Snowflake's managed NiFi is Openflow.

If the job is "Cloudera NiFi we already staff," DataFlow is the narrower tool. If the job is live SQL on Iceberg you run, DataFlow 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 Cloudera DataFlow if you want managed NiFi on Cloudera, and provenance plus the processor list are the job.

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

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