62:I[707087,["/_next/static/immutable/chunks/3arlv5bvukoo2.js","/_next/static/immutable/chunks/39v64hmfyo-sr.js","/_next/static/immutable/chunks/2nvof3-29tvc3.js","/_next/static/immutable/chunks/3tjobt77z_9dj.js","/_next/static/immutable/chunks/04j_o97cgxn2x.js","/_next/static/immutable/chunks/3upr08c9vtj8w.js","/_next/static/immutable/chunks/1ww0328si-u8x.js","/_next/static/immutable/chunks/11mfl5ymqld9-.js","/_next/static/immutable/chunks/2xxcqy6ppds3l.js","/_next/static/immutable/chunks/1bpgyrs5qflhu.js","/_next/static/immutable/chunks/0-6vu2ul1h6ld.js","/_next/static/immutable/chunks/43_s7jweyypoh.js","/_next/static/immutable/chunks/0p6vao205o1jc.js","/_next/static/immutable/chunks/1_kyvgkyokw92.js","/_next/static/immutable/chunks/1lo-zn37dolb5.js","/_next/static/immutable/chunks/1v8k6yxw1rg0u.js"],"RegistryDependencies"] 63:I[314491,["/_next/static/immutable/chunks/3arlv5bvukoo2.js","/_next/static/immutable/chunks/39v64hmfyo-sr.js","/_next/static/immutable/chunks/2nvof3-29tvc3.js","/_next/static/immutable/chunks/3tjobt77z_9dj.js","/_next/static/immutable/chunks/04j_o97cgxn2x.js","/_next/static/immutable/chunks/3upr08c9vtj8w.js","/_next/static/immutable/chunks/1ww0328si-u8x.js","/_next/static/immutable/chunks/11mfl5ymqld9-.js","/_next/static/immutable/chunks/2xxcqy6ppds3l.js","/_next/static/immutable/chunks/1bpgyrs5qflhu.js","/_next/static/immutable/chunks/0-6vu2ul1h6ld.js","/_next/static/immutable/chunks/43_s7jweyypoh.js","/_next/static/immutable/chunks/0p6vao205o1jc.js","/_next/static/immutable/chunks/1_kyvgkyokw92.js","/_next/static/immutable/chunks/1lo-zn37dolb5.js","/_next/static/immutable/chunks/1v8k6yxw1rg0u.js"],"FilesView"] 64:Tda3e,'use client' import * as React from 'react' import { Activity, AlertCircle, ArrowRight, ArrowRightLeft, BarChart3, Check, CheckCircle2, Clock, Coins, Copy, CreditCard, Database, ExternalLink, Eye, FileCode2, GitFork, HelpCircle, Key, Landmark, Layers, LineChart, Play, RefreshCw, Search, ShieldCheck, Table2, Terminal, Users, Workflow, X, } from 'lucide-react' import { cn } from '@/lib/utils' import { Badge } from '@/components/ui/badge' import { Button } from '@/components/ui/button' import { Card, CardContent, CardDescription, CardHeader, CardTitle } from '@/components/ui/card' import { Input } from '@/components/ui/input' import { Separator } from '@/components/ui/separator' import { Table, TableBody, TableCell, TableHead, TableHeader, TableRow } from '@/components/ui/table' import { Tabs, TabsContent, TabsList, TabsTrigger } from '@/components/ui/tabs' export interface ColumnDef { name: string type: string nullable: boolean isPrimaryKey?: boolean description: string sampleValue: string distinctPct: string constraints?: string } export interface QualityAssertion { id: string name: string type: string targetColumn: string description: string executionTime: string lastRun: string status: 'passed' | 'warning' | 'failed' ruleDefinition: string } export interface LineageSourceNode { id: string name: string schema: string connector: string type: string rowCount: string latency: string status: 'fresh' | 'syncing' | 'delayed' iconName: 'CreditCard' | 'Users' | 'Landmark' description: string } export interface LineageTransformNode { id: string name: string modelType: string engine: string runtime: string lastRun: string testsPassed: string status: 'passed' | 'running' | 'failed' iconName: 'Layers' | 'Coins' description: string } export interface LineageConsumerNode { id: string name: string category: string tool: string frequency: string audience: string status: 'active' | 'syncing' iconName: 'BarChart3' | 'ArrowRightLeft' | 'LineChart' description: string } export interface DataCatalogLineageProps { className?: string } const sourceNodes: LineageSourceNode[] = [ { id: 'src-stripe', name: 'raw_stripe.charges', schema: 'raw_stripe', connector: 'Fivetran CDC', type: 'PostgreSQL Raw', rowCount: '1.42M rows', latency: '~5m sync', status: 'fresh', iconName: 'CreditCard', description: 'Direct transactional charge events, currency conversions, refunds, and card processing fees.', }, { id: 'src-salesforce', name: 'raw_salesforce.accounts', schema: 'raw_salesforce', connector: 'Airbyte Cloud', type: 'CRM Extract', rowCount: '84.5k rows', latency: '~15m sync', status: 'fresh', iconName: 'Users', description: 'Enterprise accounts, contract values, billing contacts, and subscription tier assignments.', }, { id: 'src-bank', name: 'raw_bank.transfers', schema: 'raw_bank', connector: 'Kafka Stream', type: 'Banking API', rowCount: '620k rows', latency: '<1m latency', status: 'fresh', iconName: 'Landmark', description: 'Direct bank wire settlement entries, ACH clearances, and treasury reconciliation logs.', }, ] const transformNodes: LineageTransformNode[] = [ { id: 'tr-daily-revenue', name: 'dbt.int_daily_revenue', modelType: 'dbt Incremental', engine: 'dbt Core 1.8', runtime: '1m 24s', lastRun: '22m ago', testsPassed: '14/14 Passed', status: 'passed', iconName: 'Layers', description: 'Aggregates daily charge events into standardized GAAP revenue recognized per customer.', }, { id: 'tr-fx-rates', name: 'dbt.int_fx_normalized_rates', modelType: 'dbt View', engine: 'dbt Core 1.8', runtime: '18s', lastRun: '25m ago', testsPassed: '6/6 Passed', status: 'passed', iconName: 'Coins', description: 'Normalized exchange rate matrix cross-referencing European Central Bank & Federal Reserve feeds.', }, ] const consumerNodes: LineageConsumerNode[] = [ { id: 'cons-metabase', name: 'Executive Board Metabase Dashboard', category: 'BI Dashboard', tool: 'Metabase Enterprise', frequency: 'Hourly auto-refresh', audience: '42 active executives', status: 'active', iconName: 'BarChart3', description: 'C-Suite executive dashboard tracking recurring revenue, net burn rate, and board deck slide metrics.', }, { id: 'cons-netsuite', name: 'Finance NetSuite Sync', category: 'Reverse ETL', tool: 'Census Reverse ETL', frequency: 'Hourly batch', audience: 'Accounting ERP Ledger', status: 'active', iconName: 'ArrowRightLeft', description: 'Automated journal entry creation and revenue recognition schedules into NetSuite ERP.', }, { id: 'cons-looker', name: 'Looker ARR Report', category: 'Looker Model', tool: 'Looker Explore', frequency: 'Live semantic model', audience: '18 connected reports', status: 'active', iconName: 'LineChart', description: 'Self-service exploration model for RevOps, cohort retention, contraction, and expansion ARR.', }, ] const columns: ColumnDef[] = [ { name: 'month_id', type: 'varchar(7)', nullable: false, isPrimaryKey: true, description: 'YYYY-MM billing calendar interval identifier (e.g. 2026-03) representing fiscal calendar close.', sampleValue: "'2026-03'", distinctPct: '100% Unique', constraints: 'PRIMARY KEY, ISO-8601', }, { name: 'gross_revenue', type: 'decimal(18,2)', nullable: false, description: 'Total unadjusted transaction volume processed across all payment gateways before churn and discounts.', sampleValue: '$1,842,500.00', distinctPct: '99.8% Distinct', constraints: 'CHECK (gross_revenue >= 0)', }, { name: 'net_arr', type: 'decimal(18,2)', nullable: false, description: 'Annualized recurring revenue normalized across all active multi-year and monthly enterprise contracts.', sampleValue: '$14,680,240.00', distinctPct: '98.9% Distinct', constraints: 'CHECK (net_arr >= 0)', }, { name: 'churned_mrr', type: 'decimal(18,2)', nullable: true, description: 'Monthly recurring revenue lost in the period due to voluntary cancellations or involuntary card downgrades.', sampleValue: '-$12,450.00', distinctPct: '42.1% Distinct', constraints: 'CHECK (churned_mrr <= 0)', }, { name: 'active_subscriptions', type: 'integer', nullable: false, description: 'Total count of distinct active paying enterprise accounts and self-serve team workspaces.', sampleValue: '3,842', distinctPct: '100% Integer', constraints: 'CHECK (active_subscriptions > 0)', }, ] const qualityAssertions: QualityAssertion[] = [ { id: 'test-1', name: 'assert_unique_month_id', type: 'Primary Key Uniqueness', targetColumn: 'month_id', description: 'Verifies that every monthly fiscal partition key is strictly unique with 0 duplicate rows.', executionTime: '420ms', lastRun: '18m ago', status: 'passed', ruleDefinition: 'dbt_expectations.expect_compound_columns_to_be_unique(month_id)', }, { id: 'test-2', name: 'assert_positive_gross_revenue', type: 'Value Range Check', targetColumn: 'gross_revenue', description: 'Ensures that processed monthly gross revenue cannot be negative under GAAP rules.', executionTime: '380ms', lastRun: '18m ago', status: 'passed', ruleDefinition: 'dbt_expectations.expect_column_values_to_be_between(min_value=0)', }, { id: 'test-3', name: 'assert_foreign_key_accounts_integrity', type: 'Referential Integrity', targetColumn: 'account_id (upstream join)', description: 'Validates that 100% of underlying subscription revenue lines match valid accounts in Salesforce CRM.', executionTime: '890ms', lastRun: '18m ago', status: 'passed', ruleDefinition: "dbt_utils.relationships(to=ref('raw_salesforce_accounts'), field='id')", }, { id: 'test-4', name: 'assert_not_null_critical_metrics', type: 'Completeness / Non-Null', targetColumn: 'month_id, gross_revenue, net_arr', description: 'Guarantees zero null occurrences across high-priority C-Suite revenue and ARR metrics.', executionTime: '310ms', lastRun: '18m ago', status: 'passed', ruleDefinition: 'dbt.test_not_null_composite(columns=[month_id, gross_revenue, net_arr])', }, { id: 'test-5', name: 'assert_valid_iso_month_format', type: 'Regex Format Validation', targetColumn: 'month_id', description: 'Asserts all month partition tokens adhere to standard ISO format regex `^\\d{4}-(0[1-9]|1[0-2])$`.', executionTime: '240ms', lastRun: '18m ago', status: 'passed', ruleDefinition: 'dbt_expectations.expect_column_values_to_match_regex(pattern="^\\\\d{4}-(0[1-9]|1[0-2])$")', }, { id: 'test-6', name: 'assert_monotonic_active_subscriptions', type: 'Anomaly Detection (3-Sigma)', targetColumn: 'active_subscriptions', description: 'Monte Carlo ML rule checking month-over-month subscription volatility is within 3-sigma confidence bounds.', executionTime: '610ms', lastRun: '18m ago', status: 'passed', ruleDefinition: 'monte_carlo.metric_anomaly_detector(threshold_sigma=3.0, sensitivity=0.95)', }, ] export function DataCatalogLineage({ className }: DataCatalogLineageProps) { const [activeTab, setActiveTab] = React.useState('schema') const [searchQuery, setSearchQuery] = React.useState('') const [copiedRef, setCopiedRef] = React.useState(false) const [copiedQuery, setCopiedQuery] = React.useState(false) const [isRunningTests, setIsRunningTests] = React.useState(false) const [isQueryModalOpen, setIsQueryModalOpen] = React.useState(false) const [selectedNodeId, setSelectedNodeId] = React.useState('target-table') const [filterSeverity, setFilterSeverity] = React.useState<'all' | 'critical' | 'schema'>('all') const filteredColumns = React.useMemo(() => { const query = searchQuery.trim().toLowerCase() if (!query) return columns return columns.filter( (col) => col.name.toLowerCase().includes(query) || col.type.toLowerCase().includes(query) || col.description.toLowerCase().includes(query), ) }, [searchQuery]) const filteredQualityTests = React.useMemo(() => { if (filterSeverity === 'critical') { return qualityAssertions.filter((t) => t.type.includes('Primary') || t.type.includes('Referential')) } if (filterSeverity === 'schema') { return qualityAssertions.filter((t) => t.type.includes('Format') || t.type.includes('Completeness')) } return qualityAssertions }, [filterSeverity]) const selectedNodeDetails = React.useMemo(() => { if (selectedNodeId === 'target-table') { return { title: 'analytics.fct_monthly_financials', badge: 'Active Target Table', description: 'Gold-tier core financial mart consolidating Stripe billing, FX rates, and Salesforce CRM accounts for executive reporting.', metadata: [ { label: 'Layer', value: 'Marts / Core Analytics' }, { label: 'Schema', value: 'production_dw' }, { label: 'Total Rows', value: '240,000' }, { label: 'Freshness SLA', value: '60 mins (Current: 18m)' }, ], } } const src = sourceNodes.find((n) => n.id === selectedNodeId) if (src) { return { title: src.name, badge: 'Source Table', description: src.description, metadata: [ { label: 'Connector', value: src.connector }, { label: 'Ingestion Type', value: src.type }, { label: 'Volume', value: src.rowCount }, { label: 'Sync Cadence', value: src.latency }, ], } } const tr = transformNodes.find((n) => n.id === selectedNodeId) if (tr) { return { title: tr.name, badge: 'dbt Intermediate Model', description: tr.description, metadata: [ { label: 'Engine', value: tr.engine }, { label: 'Materialization', value: tr.modelType }, { label: 'Build Duration', value: tr.runtime }, { label: 'Test Suite', value: tr.testsPassed }, ], } } const cons = consumerNodes.find((n) => n.id === selectedNodeId) if (cons) { return { title: cons.name, badge: 'Downstream Consumer', description: cons.description, metadata: [ { label: 'Tool Platform', value: cons.tool }, { label: 'Type', value: cons.category }, { label: 'Audience / Usage', value: cons.audience }, { label: 'Sync Schedule', value: cons.frequency }, ], } } return null }, [selectedNodeId]) const copyRef = React.useCallback((text: string) => { if (typeof navigator !== 'undefined' && navigator.clipboard) { navigator.clipboard.writeText(text) setCopiedRef(true) setTimeout(() => { setCopiedRef(false) }, 2000) } }, []) const copyCompiledSQL = React.useCallback(() => { const sql = `{{ config( materialized='incremental', unique_key='month_id', schema='production_dw', tags=['finance', 'tier_1_gold', 'executive_kpi'] ) }} WITH daily_rev AS ( SELECT * FROM {{ ref('int_daily_revenue') }} {% if is_incremental() %} WHERE event_date >= dateadd(day, -3, current_date) {% endif %} ), fx_rates AS ( SELECT * FROM {{ ref('int_fx_normalized_rates') }} ), monthly_aggregates AS ( SELECT TO_CHAR(d.event_date, 'YYYY-MM') AS month_id, SUM(d.gross_amount_usd) AS gross_revenue, SUM(d.annualized_contract_val) AS net_arr, SUM(CASE WHEN d.is_churned THEN d.mrr_impact ELSE 0 END) AS churned_mrr, COUNT(DISTINCT d.customer_account_id) AS active_subscriptions FROM daily_rev d LEFT JOIN fx_rates fx ON d.currency = fx.currency AND d.event_date = fx.rate_date GROUP BY 1 ) SELECT month_id, ROUND(gross_revenue, 2) AS gross_revenue, ROUND(net_arr, 2) AS net_arr, ROUND(churned_mrr, 2) AS churned_mrr, active_subscriptions FROM monthly_aggregates ORDER BY month_id DESC;` if (typeof navigator !== 'undefined' && navigator.clipboard) { navigator.clipboard.writeText(sql) setCopiedQuery(true) setTimeout(() => { setCopiedQuery(false) }, 2000) } }, []) const runSimulatedTests = React.useCallback(() => { setIsRunningTests(true) setTimeout(() => { setIsRunningTests(false) }, 1200) }, []) return (

{/* Header Section */}

{/* Cluster / Warehouse breadcrumbs */}

snowflake-dw-production.us-east-1 / schema: production_dw Tier 1 Core Mart

{/* Table Title and Badges */}

analytics.fct_monthly_financials

{/* Metadata Badges: Freshness, SLA, Owner */}

{/* Data Freshness */}

Freshness: Updated 18m ago SLA: Healthy

{/* Table Owner */}

Owner: Data Platform Squad #data-eng-core

{/* Stats */}

Rows: 240,000 5 columns

{/* Header Actions */}

{/* Interactive SQL Query Drawer / Bar */} {isQueryModalOpen && (

Snowflake SQL Console · Interactive Query Runner Snowflake XS Warehouse

                {`SELECT
  month_id,
  gross_revenue,
  net_arr,
  churned_mrr,
  active_subscriptions
FROM production_dw.analytics.fct_monthly_financials
ORDER BY month_id DESC
LIMIT 5;`}

{/* Query Results Preview */}

month_id (varchar) gross_revenue (decimal) net_arr (decimal) churned_mrr (decimal) active_subscriptions (int) 2026-03 $1,842,500.00 $14,680,240.00 -$12,450.00 3,842 2026-02 $1,798,200.00 $14,350,100.00 -$14,100.00 3,780 2026-01 $1,720,000.00 $13,920,400.00 -$9,800.00 3,712

)} {/* 3-Column Lineage Pipeline Map Section */}

End-to-End Data Lineage Graph

DAG Depth: 3 Tiers

Interactive trace of upstream ingest sources, dbt intermediate transformations, and active downstream BI consumers.

Pipeline State: All 3 Tiers Fresh

{/* 3-Column Pipeline Map Grid */}

{/* COLUMN 1: Source Tables (Left 1/3) */}

1. Source Tables (3)

Upstream Raw

{sourceNodes.map((source) => (

setSelectedNodeId(source.id)} >

{source.iconName === 'CreditCard' ? ( ) : source.iconName === 'Users' ? ( ) : ( )}

{source.name} {source.connector}

{source.status}

{source.rowCount} {source.latency}

))}

{/* COLUMN 2: Transform Models (Center 1/3) */}

2. Transform Models (2)

dbt Models

{transformNodes.map((transform) => (

setSelectedNodeId(transform.id)} >

{transform.iconName === 'Layers' ? ( ) : ( )}

{transform.name} {transform.modelType}

{transform.runtime}

{transform.testsPassed} {transform.lastRun}

))}

{/* COLUMN 3: Downstream Consumers & Active Target (Right 1/3) */}

3. Downstream & Target

Marts & Consumers

{/* Active Target Node Highlight */}

setSelectedNodeId('target-table')} >

fct_monthly_financials

Active Catalog Target

Current

{/* Downstream Consumers */} {consumerNodes.map((consumer) => (

setSelectedNodeId(consumer.id)} >

{consumer.iconName === 'BarChart3' ? ( ) : consumer.iconName === 'ArrowRightLeft' ? ( ) : ( )}

{consumer.name} {consumer.category} · {consumer.tool}

{consumer.status}

{consumer.audience} {consumer.frequency}

))}

{/* Interactive Node Inspector Drawer / Card */} {selectedNodeDetails && (

{selectedNodeDetails.title} {selectedNodeDetails.badge}

Node Inspector

{selectedNodeDetails.description}

{selectedNodeDetails.metadata.map((item) => (

{item.label}: {item.value}

))}

)}

{/* Table Schema, Quality Tests & Governance Tabs */} Schema & Columns {columns.length} Quality Tests 6/6 dbt Model SQL SLA & Governance {/* TAB 1: Schema & Column Definitions */} {/* Controls Bar */}

setSearchQuery(e.target.value)} placeholder="Search columns, types, or terms..." className="h-8.5 pl-8 text-xs" />

Showing {filteredColumns.length} of{' '} {columns.length} columns

{/* Columns Table Card */} Column Name Data Type Nullability Key / Constraint Sample Value Description {filteredColumns.map((col) => (

{col.isPrimaryKey && } {{ name: col.name }.name}

{col.type} {!col.nullable ? ( NOT NULL ) : ( NULLABLE )}

{col.isPrimaryKey ? ( PRIMARY KEY ) : col.constraints ? ( {col.constraints} ) : ( — )}

{col.sampleValue} {col.description} ))}

{/* Summary Stat Cards */}

Total Schema Columns 5 1 Primary Key, 4 Measures

Completeness Score 97.5% 4/5 Non-Nullable fields

Table Storage Volume 48.6 MB Micro-partition compressed

Governance Tier Gold Tier 1 Certified Financial Source

{/* TAB 2: Data Quality Tests Card (6 Passing Assertions) */} {/* Quality Health Summary Card */}

Data Quality & Integrity Assertions 100% Passing (6/6)

Automated dbt-expectations, Monte Carlo ML anomaly detection, and referential integrity test suite.

{/* Filter Pills */}

{/* Assertions List */}

{filteredQualityTests.map((assertion) => (

{assertion.name} {assertion.type} target: {assertion.targetColumn}

{assertion.description}

{assertion.ruleDefinition}

Duration {assertion.executionTime}

Passed

))}

{/* TAB 3: Compiled SQL / dbt Model */}

models/marts/finance/fct_monthly_financials.sql dbt v1.8 Incremental

                {`{{ config(
    materialized='incremental',
    unique_key='month_id',
    schema='production_dw',
    tags=['finance', 'tier_1_gold', 'executive_kpi']
) }}

WITH daily_rev AS (
    SELECT * FROM {{ ref('int_daily_revenue') }}
    {% if is_incremental() %}
    WHERE event_date >= dateadd(day, -3, current_date)
    {% endif %}
),

fx_rates AS (
    SELECT * FROM {{ ref('int_fx_normalized_rates') }}
),

monthly_aggregates AS (
    SELECT
        TO_CHAR(d.event_date, 'YYYY-MM') AS month_id,
        SUM(d.gross_amount_usd) AS gross_revenue,
        SUM(d.annualized_contract_val) AS net_arr,
        SUM(CASE WHEN d.is_churned THEN d.mrr_impact ELSE 0 END) AS churned_mrr,
        COUNT(DISTINCT d.customer_account_id) AS active_subscriptions
    FROM daily_rev d
    LEFT JOIN fx_rates fx ON d.currency = fx.currency AND d.event_date = fx.rate_date
    GROUP BY 1
)

SELECT
    month_id,
    ROUND(gross_revenue, 2) AS gross_revenue,
    ROUND(net_arr, 2) AS net_arr,
    ROUND(churned_mrr, 2) AS churned_mrr,
    active_subscriptions
FROM monthly_aggregates
ORDER BY month_id DESC;`}

{/* TAB 4: SLA & Governance */}

{/* Ownership & Contacts */}

Table Ownership & Escalation

Owning Team: Data Platform Squad

Technical Lead: @sarah.lin (Principal Data Eng)

Slack Channel: #data-platform-alerts

On-Call Rotation: PagerDuty · Tier 1 High Severity

{/* Freshness SLA Target */}

Freshness SLA & Cadence

Target SLA Threshold: < 60 minutes

Current Latency: 18 minutes (Compliant)

Cron Schedule:


                    0 * * * * (Hourly)

Historical Uptime: 99.98% (Last 90 days)

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