Coefficient alternative — Coefficient vs Skippr

Coefficient is a Google Sheets and Excel add-on. You pull Salesforce, HubSpot, ads, or SQL into a sheet, refresh on a schedule, and write some of it back. 150+ connectors. Free is three standard sources, 5,000 import rows, 50 manual refreshes a month. Starter is 49 USD a month: daily auto-refresh, snapshots, SQL and pivot builders. Pro is 99 USD per user per month (five users max): six standard sources, three accounts each, hourly refresh, unlimited import rows, shared connections. Enterprise adds premium sources (Snowflake, NetSuite, Sage Intacct, BigQuery, Databricks, and similar), SSO, and unlimited refreshes. A 30-day Pro trial, then Free if you do not pick a plan. Annual billing is 17% off.

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 per-user sheet licence. ELT into a warehouse is one path, not the product.

Feature	Skippr	Coefficient
Deploy	Single Rust binary, your VPC or on-prem	Sheets / Excel add-on. SaaS only
Schema	In-stream evolution, sibling columns	Import columns you pick. Formulas stay on refresh
Storage	Open Iceberg on Parquet you own	The spreadsheet. Warehouse sources are Enterprise
CDC	Log-based; fails closed if the dest cannot honour	Scheduled refresh. Not log CDC
Query	`skipprd query` on Iceberg ∪ WAL; history in any Iceberg engine	The sheet. SQL builder pulls into cells
Pricing	Free self-hosted engine	Free, then 49 or 99 USD a user a month. See their pricing.

What Coefficient is good at

The spreadsheet is the UI. A RevOps lead can import Salesforce reports, keep formulas, and push cleaned rows back without leaving Sheets. Alerts to Slack or email sit on the same add-on. For FP&A and pipeline reviews that already live in Excel, that is a real product. The free plan is enough to try a source.

Where the job is wider

Sources, accounts, refresh counts, and row caps are the meter. Hourly is Pro. Snowflake and NetSuite sit on Enterprise. Five users is the Pro ceiling. This is last-mile reporting, not a lake. There is no Iceberg ∪ WAL. There is no binary in your VPC. Query is a cell.

Skippr will not give you a Sheets ribbon or Salesforce writeback from Excel. If the job is "live Salesforce in the weekly forecast sheet," Coefficient is the narrower tool. If the job is live SQL on Iceberg you run, Coefficient 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.

YAMLCLIPython

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 Coefficient if the destination is a spreadsheet, and Salesforce or HubSpot in Sheets is the job.

Choose Skippr if you want one binary, no per-user sheet licence, live SQL, and Iceberg files that stay yours.

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

Coefficient alternative — Coefficient vs Skippr ​

What Coefficient is good at ​

Where the job is wider ​

Getting started ​

Which to choose ​

Coefficient alternative — Coefficient vs Skippr

What Coefficient is good at

Where the job is wider

Getting started

Which to choose