K2view alternative — K2view vs Skippr

K2view is an entity fabric. Fabric builds a Micro-Database (a Logical Unit Instance) for each business entity: one customer, one order, one claim. That file is SQLite. It holds every source row that belongs to that entity, can sit in RAM, and answers SQL about that one entity. Instances are compressed as blob chunks in the LU storage layer. You can encrypt a MicroDB or a field. CDC in Fabric is two directions: inbound updates arrive on a message queue, and any MicroDB change can publish to a queue for subscribers. Consumers also reach data through APIs, SQL, messaging, streaming, and MCP. The same platform sells test-data management, masking, tokenisation, and synthetic data. K2view does not publish a price list on its site. AWS Marketplace SaaS starts at 120,000 USD a year as a private offer, then meters usage credits (0.01 USD each), Micro-DB create/update ops (4 USD per 100,000), consume ops (2 USD per 100,000), stored gigabytes (0.50 USD), transfer (0.11 USD per GB), plus 100 USD per dev environment and 800 USD per production environment. This is not a Streamhouse and it is not Iceberg.

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

Feature	Skippr	K2view
Deploy	Single Rust binary, your VPC or on-prem	Fabric cluster or K2view SaaS
Schema	In-stream evolution, sibling columns	Logical Unit plus a semantic layer over sources
Storage	Open Iceberg on Parquet you own	One SQLite MicroDB per entity, stored as blobs
CDC	Log-based; fails closed if the dest cannot honour	Queue in, queue out of the MicroDB
Query	skipprd query on Iceberg ∪ WAL; history in any Iceberg engine	SQL/API on one entity. Not lake SQL across Iceberg
Pricing	Free self-hosted engine	Quote. Marketplace SaaS from 120,000 USD/year plus ops.

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What K2view is good at

A 360 of one customer without joining five systems at request time. That is the product. Isolation per entity is why telco and bank operational fabrics still buy it: one claim, one encryption boundary, one millisecond read. Test data and masking on the same LU is a real adjacent job. If the job is "serve this customer's data to a channel," that is a real product.

Where the job is wider

K2view is an entity store. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query across a lake. You agreed the entity first; then Fabric builds the LU.

Ops math is the meter. Create/update and consume are separate 100,000-op lines. Storage and transfer stack on top. A private-offer floor of 120,000 USD a year is the Marketplace starting point, not a public card on k2view.com.

Skippr will not build a MicroDB per customer. Skippr also will not mask test data. If the job is "operational 360 by entity," K2view is the narrower tool. If the job is live SQL on Iceberg you run, K2view 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 K2view if you need an operational 360 per business entity, and SQL or an API on that entity is enough.

Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not build a Micro-Database.

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