Delta Lake alternative — Delta Lake vs Skippr

Delta Lake is an Apache-2.0 table format (Linux Foundation). Current 4.x tip is 4.4.1 (5 Oct 2026) on Spark 4.0 / 4.1 / 4.2. The 3.3.3 line (12 Aug 2026) remains for Spark 3.x. The transaction log sits beside Parquet. Spark is the native writer. 4.4.0 added Spark 4.2 support, identity columns in SQL DDL, and Unity Catalog Delta API wiring for Delta Kernel and an experimental Flink connector. UniForm generates Iceberg (and preview Hudi) metadata asynchronously so those clients can read the same Parquet. Iceberg UniForm needs column mapping, minReaderVersion at least 2, minWriterVersion at least 7, Delta Lake 3.1+ writes, IcebergCompatV2, and historically Hive Metastore as the Iceberg catalog. UniForm does not work with deletion vectors. Iceberg and Hudi clients are read-only. A foreign Iceberg writer can garbage-collect files Delta still needs. CDC is not Delta. Spark Structured Streaming, Auto Loader, or Databricks Lakeflow still ingest. There is no licence fee for the format. You pay Spark (or Databricks DBUs), storage, and people. This is not Iceberg ∪ WAL. Serving state is the Delta log.

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

Feature	Skippr	Delta Lake
Deploy	Single Rust binary, your VPC or on-prem	Spark (or Databricks / Flink experimental) plus a catalog
Schema	In-stream evolution, sibling columns	Delta schema enforcement and evolution. Variant on 4.x
Storage	Open Iceberg on Parquet you own	Delta log plus Parquet. UniForm Iceberg is a reader view
CDC	Log-based; fails closed if the dest cannot honour	A Spark/Lakeflow source writes Delta. Not Postgres WAL
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	Spark/Databricks on Delta. Iceberg engines via UniForm (read)
Pricing	Free self-hosted engine	Apache-2.0. Pay Spark. Databricks is DBU.

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What Delta Lake is good at

Spark MERGE into a lake table your Databricks or OSS Spark jobs already own, with time travel on the Delta log. That is the product. UniForm is the honest Iceberg-reader path when the writer stays Delta. If the job is "this Spark table as Delta," that is a real product.

Where the job is wider

Delta is a table format. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight Iceberg rows. Postgres CDC is Lakeflow, DMS, or Debezium in front of Spark, not the format. UniForm Iceberg is read-only and incompatible with deletion vectors. A second writer on the Iceberg view is unsafe.

Spark math is the ops. Protocol versions, column mapping, UniForm conversion, and Unity Catalog if you are on Databricks. Skippr will not write Delta. Skippr also will not replace a Unity Catalog managed Delta table. Lakeflow is a separate page. Spark Structured Streaming is another.

If the job is "Delta we already staff," Delta is the narrower tool. If the job is live SQL on Iceberg you run, UniForm 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 Delta Lake if Spark (or Databricks) already owns the table, and the Delta log is the contract.

Choose Skippr if you want one binary, live SQL on Iceberg ∪ WAL, and files that stay yours. Skippr will not write a Delta table.

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