acuantia-dataform

name	acuantia-dataform
description	Use when working on Acuantia's BigQuery Dataform pipeline (acuantia-gcp-dataform project) - adds Acuantia-specific patterns on top of dataform-engineering-fundamentals: ODS two-arg ref() syntax, looker_ filename prefix, Looker integration (looker_prod/looker_dev), acuantia dataset conventions, coordination with callrail_data_export/dialpad_data_integration/looker projects

Acuantia Dataform Engineering

REQUIRED PREREQUISITE

YOU MUST USE dataform-engineering-fundamentals SKILL FIRST.

This skill is a thin extension layer that adds Acuantia-specific patterns on top of the generic dataform-engineering-fundamentals skill.

Before using this skill:

1. Read and follow dataform-engineering-fundamentals completely
2. Apply ALL generic Dataform practices from that skill
3. Then apply the Acuantia-specific patterns below

This skill does NOT repeat generic practices. If you're looking for:

• TDD workflow → See dataform-engineering-fundamentals
• Safety practices (--schema-suffix dev, --dry-run) → See dataform-engineering-fundamentals
• ${ref()} enforcement → See dataform-engineering-fundamentals
• Documentation standards → See dataform-engineering-fundamentals
• Architecture patterns → See dataform-engineering-fundamentals

This skill ONLY adds: Acuantia-specific conventions that differ from or extend generic patterns.

When to Use

Use this skill when working on:

• acuantia-gcp-dataform project
• Tables that integrate with Acuantia's Looker instance
• Transformations using Acuantia's ODS (Operational Data Store) architecture
• Pipelines coordinating with callrail_data_export or dialpad_data_integration projects

Acuantia-Specific Patterns

1. ODS Architecture and Two-Argument ref()

Acuantia uses a special ODS (Operational Data Store) architecture that requires two-argument ref() syntax.

ODS Architecture:

• acuantia.ods - Source of truth (master operational data)
• acuantia.ods_dev - Development/staging dataset
• acuantia.ods_prod - Production staging dataset

CRITICAL: Use two-argument ref() for ODS tables to avoid suffix duplication:

-- CORRECT: Two-argument ref() for ODS
FROM ${ref("ods", "sap_customers")}
FROM ${ref("ods", "magento_orders")}

-- WRONG: Single-argument causes ods_dev_dev with --schema-suffix dev
FROM ${ref("sap_customers")}  -- Creates ods_dev_dev ❌

Why: The ODS schema name itself gets the suffix applied. Two-argument ref() prevents ods_dev_dev when using --schema-suffix dev.

All other tables: Use single-argument ref() as per dataform-engineering-fundamentals.

2. Looker Table Naming Convention

Tables in definitions/output/looker/ MUST be prefixed with looker_.

File naming:

definitions/output/looker/looker_customer_metrics.sqlx  ✅
definitions/output/looker/looker_sales_summary.sqlx     ✅
definitions/output/looker/customer_metrics.sqlx         ❌

Schema configuration:

config {
  type: "table",
  schema: "looker_prod",  // Production Looker tables
  tags: ["looker", "daily"]
}

Why:

• Makes Looker-specific tables immediately identifiable
• Prevents naming conflicts with intermediate tables
• Aligns with Looker project conventions in looker/ directory

3. Acuantia Dataset Conventions

Primary Datasets:

• acuantia.ods - Master operational data store (source of truth)
• acuantia.ods_dev / acuantia.ods_prod - ODS staging datasets
• acuantia.looker_prod - Production Looker tables
• acuantia.looker_dev - Development Looker tables
• acuantia.dataform - Operations and temp tables
• acuantia.callrail_api - CallRail raw data
• acuantia.dialpad_api - Dialpad raw data
• acuantia.hubspot - HubSpot data (via Fivetran)
• acuantia.magento_rotoplas_me_22_prod - Magento/Adobe Commerce data (via Fivetran)

Schema suffix behavior:

# With --schema-suffix dev
looker_prod → looker_dev
ods → ods (no suffix, use two-arg ref)
dataform → dataform_dev

4. Looker Integration Context

Tables in definitions/output/looker/ feed Acuantia's Looker instance at https://looker.acuantia.com.

Optimization requirements:

• Add partitioning/clustering for query performance (Looker users run ad-hoc queries)
• Use descriptive column names (Looker dimension names derive from these)
• Include comprehensive column descriptions (synced to Looker metadata via scripts)
• Consider common Looker user query patterns (filters, aggregations)

Looker-specific config pattern:

config {
  type: "table",
  schema: "looker_prod",
  tags: ["looker", "daily"],
  bigquery: {
    partitionBy: "DATE(order_date)",
    clusterBy: ["customer_id", "region"]
  },
  columns: {
    customer_id: "Unique customer identifier from SAP (KUNNR field)",
    order_date: "Date when order was placed",
    region: "Geographic region for reporting (matches Looker region dimension)"
  }
}

Metadata sync: Use node scripts/updateLookerDescriptions.js in acuantia-gcp-dataform to sync column descriptions to Looker views.

5. Source System Integration

Acuantia integrates data from multiple source systems. Use specific terminology when documenting columns:

SAP ERP:

columns: {
  customer_id: "SAP Customer Number (KUNNR) - unique identifier in SAP ERP",
  customer_name: "Customer name (NAME1 field) - legal business name",
  account_group: "Customer Account Group (KTOKD) - classification code"
}

Dialpad API (from dialpad_data_integration project):

-- Source declaration
-- definitions/sources/dialpad/calls.sqlx
config {
  type: "declaration",
  database: "acuantia",
  schema: "dialpad_api",
  name: "calls",
  description: "Dialpad call records with transcripts and sentiment analysis",
  columns: {
    call_id: "Unique call identifier from Dialpad API",
    transcript: "Full call transcript from Dialpad AI",
    sentiment: "Overall call sentiment: positive/negative/neutral/mixed"
  }
}

CallRail API (from callrail_data_export project):

-- Source declaration
-- definitions/sources/callrail/calls.sqlx
config {
  type: "declaration",
  database: "acuantia",
  schema: "callrail_api",
  name: "calls",
  columns: {
    call_id: "Unique CallRail call identifier",
    tracking_phone_number: "CallRail tracking number that received the call",
    attribution: "Nested attribution data (source, medium, campaign)"
  }
}

HubSpot CRM (via Fivetran):

config {
  type: "declaration",
  database: "acuantia",
  schema: "hubspot",
  name: "contact"
}

Magento/Adobe Commerce (via Fivetran):

config {
  type: "declaration",
  database: "acuantia",
  schema: "magento_rotoplas_me_22_prod",
  name: "sales_order"
}

6. Cross-Project Coordination

Acuantia's data platform spans multiple projects that work together:

callrail_data_export/          → acuantia.callrail_api.*
dialpad_data_integration/      → acuantia.dialpad_api.*
acuantia-gcp-dataform/         → Transform and model
looker/                        → Visualize and report

When modifying schemas:

1. Source changes (callrail_data_export or dialpad_data_integration):

   • Update Python schema definitions
   • Test with small data exports
   • Deploy to production

2. Dataform updates (acuantia-gcp-dataform):

   • Update source declarations in definitions/sources/
   • Modify transformations if needed
   • Update definitions/output/looker/ tables
   • Test with --schema-suffix dev

3. Looker updates (looker project):

   • Update view definitions
   • Add new dimensions/measures
   • Test in development environment

Schema change protocol: Always coordinate changes across all three layers (raw → transformed → visualization).

7. Business Context

Acuantia serves four main product verticals:

• Septic: Septic tank systems
• General: General purpose containers
• Industrial: Industrial containers and equipment
• Chemical: Chemical storage containers

Key business entities:

• TankHolding: Key business vertical with specialized recovery operations
• Customer Journey: Multi-touch attribution across CallRail, HubSpot, and Magento
• Voice of Customer (VoC): Dialpad call transcripts analyzed for sentiment and topics

When creating tables, consider how they support these business verticals and use cases.

Validation Queries (Acuantia-Specific)

# Check Looker dev tables
bq query --use_legacy_sql=false \
  "SELECT COUNT(*) FROM \`acuantia.looker_dev.looker_customer_metrics\`"

# Check ODS tables
bq query --use_legacy_sql=false \
  "SELECT COUNT(*) FROM \`acuantia.ods.sap_customers\`"

# Verify CallRail data freshness
bq query --use_legacy_sql=false \
  "SELECT MAX(start_time) FROM \`acuantia.callrail_api.calls\`"

# Verify Dialpad data freshness
bq query --use_legacy_sql=false \
  "SELECT MAX(start_time) FROM \`acuantia.dialpad_api.calls\`"

Common Acuantia-Specific Mistakes

Mistake 1: Using single-argument ref() for ODS tables

-- WRONG: Creates ods_dev_dev with --schema-suffix dev
FROM ${ref("sap_customers")}

-- CORRECT: Two-argument ref() for ODS
FROM ${ref("ods", "sap_customers")}

Mistake 2: Missing looker_ prefix

# WRONG
definitions/output/looker/customer_metrics.sqlx

# CORRECT
definitions/output/looker/looker_customer_metrics.sqlx

Mistake 3: Using wrong schema for Looker tables

-- WRONG
config {
  type: "table",
  schema: "reporting"  // Not Looker-specific
}

-- CORRECT
config {
  type: "table",
  schema: "looker_prod"  // Consumed by Looker
}

Mistake 4: Hardcoding acuantia.ods in queries

-- WRONG: Hardcoded path
FROM `acuantia.ods.sap_customers`

-- CORRECT: Use two-argument ref()
FROM ${ref("ods", "sap_customers")}

Red Flags - Acuantia-Specific

If you're thinking any of these thoughts, STOP:

• "I'll use single-argument ref() for ODS tables (it's simpler)"
• "I don't need the looker_ prefix for this Looker table"
• "I'll use a different schema name instead of looker_prod"
• "I'll skip coordinating with the looker/ project team"
• "I don't need to check CallRail/Dialpad data freshness"

All of these mean: You're about to break Acuantia conventions. Follow the patterns above.

Quick Reference

Pattern	Acuantia Convention
ODS tables	Two-argument ref(): ${ref("ods", "table_name")}
Looker tables	Prefix with looker_ and use schema: "looker_prod"
CallRail data	acuantia.callrail_api.*
Dialpad data	acuantia.dialpad_api.*
HubSpot data	acuantia.hubspot.*
Magento data	acuantia.magento_rotoplas_me_22_prod.*
Dev testing	--schema-suffix dev (see dataform-engineering-fundamentals)
Looker metadata	Run node scripts/updateLookerDescriptions.js

Summary

This skill adds only Acuantia-specific patterns. For all generic Dataform practices:

• TDD workflow
• Safety practices
• ${ref()} enforcement (general cases)
• Documentation standards
• Architecture patterns
• Troubleshooting

→ See dataform-engineering-fundamentals skill.

The patterns in this skill (ODS two-arg ref, looker_ prefix, Acuantia datasets, cross-project coordination) are required additions to the generic foundation, not replacements.