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

\n analytics.fct_monthly_financials\n

\n copyRef('analytics.fct_monthly_financials')}\n >\n {copiedRef ? : }\n {copiedRef ? 'Copied Ref!' : 'Copy Name'}\n \n
\n\n {/* Metadata Badges: Freshness, SLA, Owner */}\n
\n {/* Data Freshness */}\n
\n \n \n \n \n Freshness:\n Updated 18m ago\n SLA: Healthy\n
\n\n {/* Table Owner */}\n
\n \n Owner:\n Data Platform Squad\n \n #data-eng-core\n \n
\n\n {/* Stats */}\n
\n \n Rows:\n 240,000\n 5 columns\n
\n
\n
\n\n {/* Header Actions */}\n
\n {\n setSelectedNodeId('target-table')\n setActiveTab('schema')\n }}\n >\n \n View Upstream Lineage\n \n\n setIsQueryModalOpen(!isQueryModalOpen)}\n >\n \n Query Table\n \n
\n
\n
\n\n {/* Interactive SQL Query Drawer / Bar */}\n {isQueryModalOpen && (\n
\n
\n
\n \n \n Snowflake SQL Console · Interactive Query Runner\n \n \n Snowflake XS Warehouse\n \n
\n
\n \n \n
\n
\n
\n
\n 
\n                {`SELECT\n  month_id,\n  gross_revenue,\n  net_arr,\n  churned_mrr,\n  active_subscriptions\nFROM production_dw.analytics.fct_monthly_financials\nORDER BY month_id DESC\nLIMIT 5;`}\n
\n
\n {/* Query Results Preview */}\n
\n \n \n \n month_id (varchar)\n gross_revenue (decimal)\n net_arr (decimal)\n churned_mrr (decimal)\n active_subscriptions (int)\n \n \n \n \n 2026-03\n $1,842,500.00\n $14,680,240.00\n -$12,450.00\n 3,842\n \n \n 2026-02\n $1,798,200.00\n $14,350,100.00\n -$14,100.00\n 3,780\n \n \n 2026-01\n $1,720,000.00\n $13,920,400.00\n -$9,800.00\n 3,712\n \n \n
\n
\n
\n
\n )}\n\n {/* 3-Column Lineage Pipeline Map Section */}\n
\n
\n
\n
\n \n

End-to-End Data Lineage Graph

\n \n DAG Depth: 3 Tiers\n \n
\n

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

\n
\n\n
\n
\n \n Pipeline State:\n All 3 Tiers Fresh\n
\n
\n
\n\n {/* 3-Column Pipeline Map Grid */}\n
\n {/* COLUMN 1: Source Tables (Left 1/3) */}\n
\n
\n
\n \n \n 1. Source Tables (3)\n \n
\n \n Upstream Raw\n \n
\n\n
\n {sourceNodes.map((source) => (\n setSelectedNodeId(source.id)}\n >\n
\n
\n
\n {source.iconName === 'CreditCard' ? (\n \n ) : source.iconName === 'Users' ? (\n \n ) : (\n \n )}\n
\n
\n {source.name}\n {source.connector}\n
\n
\n \n {source.status}\n \n
\n\n
\n {source.rowCount}\n \n \n {source.latency}\n \n
\n
\n ))}\n
\n
\n\n {/* COLUMN 2: Transform Models (Center 1/3) */}\n
\n
\n
\n \n \n 2. Transform Models (2)\n \n
\n \n dbt Models\n \n
\n\n
\n {transformNodes.map((transform) => (\n setSelectedNodeId(transform.id)}\n >\n
\n
\n
\n {transform.iconName === 'Layers' ? (\n \n ) : (\n \n )}\n
\n
\n {transform.name}\n {transform.modelType}\n
\n
\n \n {transform.runtime}\n \n
\n\n
\n \n \n {transform.testsPassed}\n \n {transform.lastRun}\n
\n
\n ))}\n
\n \n\n {/* COLUMN 3: Downstream Consumers & Active Target (Right 1/3) */}\n
\n
\n
\n \n \n 3. Downstream & Target\n \n
\n \n Marts & Consumers\n \n
\n\n
\n {/* Active Target Node Highlight */}\n setSelectedNodeId('target-table')}\n >\n
\n
\n
\n \n
\n
\n
\n fct_monthly_financials\n
\n Active Catalog Target\n
\n
\n Current\n
\n
\n\n {/* Downstream Consumers */}\n {consumerNodes.map((consumer) => (\n setSelectedNodeId(consumer.id)}\n >\n
\n
\n
\n {consumer.iconName === 'BarChart3' ? (\n \n ) : consumer.iconName === 'ArrowRightLeft' ? (\n \n ) : (\n \n )}\n
\n
\n {consumer.name}\n \n {consumer.category} · {consumer.tool}\n \n
\n
\n \n {consumer.status}\n \n
\n\n
\n {consumer.audience}\n {consumer.frequency}\n
\n
\n ))}\n \n \n \n\n {/* Interactive Node Inspector Drawer / Card */}\n {selectedNodeDetails && (\n
\n
\n
\n {selectedNodeDetails.title}\n \n {selectedNodeDetails.badge}\n \n
\n Node Inspector\n
\n

{selectedNodeDetails.description}

\n
\n {selectedNodeDetails.metadata.map((item) => (\n
\n {item.label}:\n {item.value}\n
\n ))}\n
\n
\n )}\n
\n\n {/* Table Schema, Quality Tests & Governance Tabs */}\n \n \n \n \n Schema & Columns\n {columns.length}\n \n\n \n \n Quality Tests\n 6/6\n \n\n \n \n dbt Model SQL\n \n\n \n \n SLA & Governance\n \n \n\n {/* TAB 1: Schema & Column Definitions */}\n \n {/* Controls Bar */}\n
\n
\n \n setSearchQuery(e.target.value)}\n placeholder=\"Search columns, types, or terms...\"\n className=\"h-8.5 pl-8 text-xs\"\n />\n
\n\n
\n
\n Showing {filteredColumns.length} of{' '}\n {columns.length} columns\n
\n copyRef(columns.map((c) => c.name).join(', '))}\n >\n \n Copy Columns\n \n
\n
\n\n {/* Columns Table Card */}\n \n \n \n \n Column Name\n Data Type\n Nullability\n Key / Constraint\n Sample Value\n Description\n \n \n \n {filteredColumns.map((col) => (\n \n \n
\n {col.isPrimaryKey && }\n \n {{ name: col.name }.name}\n \n
\n \n\n \n \n {col.type}\n \n \n\n \n {!col.nullable ? (\n \n NOT NULL\n \n ) : (\n NULLABLE\n )}\n \n\n \n
\n {col.isPrimaryKey ? (\n \n PRIMARY KEY\n \n ) : col.constraints ? (\n \n {col.constraints}\n \n ) : (\n —\n )}\n
\n \n\n {col.sampleValue}\n\n {col.description}\n \n ))}\n \n
\n \n\n {/* Summary Stat Cards */}\n
\n
\n Total Schema Columns\n 5\n 1 Primary Key, 4 Measures\n
\n\n
\n Completeness Score\n 97.5%\n 4/5 Non-Nullable fields\n
\n\n
\n Table Storage Volume\n 48.6 MB\n Micro-partition compressed\n
\n\n
\n Governance Tier\n Gold Tier 1\n Certified Financial Source\n
\n
\n \n\n {/* TAB 2: Data Quality Tests Card (6 Passing Assertions) */}\n \n {/* Quality Health Summary Card */}\n \n \n
\n
\n
\n \n Data Quality & Integrity Assertions\n \n 100% Passing (6/6)\n \n
\n \n Automated dbt-expectations, Monte Carlo ML anomaly detection, and referential integrity test suite.\n \n
\n\n \n \n {isRunningTests ? 'Running 6 Tests...' : 'Re-run Test Suite'}\n \n
\n \n\n \n {/* Filter Pills */}\n
\n setFilterSeverity('all')}\n >\n All Assertions (6)\n \n setFilterSeverity('critical')}\n >\n Primary Key & Referential (2)\n \n setFilterSeverity('schema')}\n >\n Format & Completeness (2)\n \n
\n\n {/* Assertions List */}\n
\n {filteredQualityTests.map((assertion) => (\n \n
\n
\n
\n \n
\n
\n
\n {assertion.name}\n \n {assertion.type}\n \n \n target: {assertion.targetColumn}\n \n
\n

{assertion.description}

\n
\n {assertion.ruleDefinition}\n
\n
\n
\n\n
\n
\n Duration\n {assertion.executionTime}\n
\n Passed\n
\n
\n
\n ))}\n \n \n \n \n\n {/* TAB 3: Compiled SQL / dbt Model */}\n \n
\n
\n
\n \n \n models/marts/finance/fct_monthly_financials.sql\n \n \n dbt v1.8 Incremental\n \n
\n \n
\n\n
\n 
\n                {`{{ config(\n    materialized='incremental',\n    unique_key='month_id',\n    schema='production_dw',\n    tags=['finance', 'tier_1_gold', 'executive_kpi']\n) }}\n\nWITH daily_rev AS (\n    SELECT * FROM {{ ref('int_daily_revenue') }}\n    {% if is_incremental() %}\n    WHERE event_date >= dateadd(day, -3, current_date)\n    {% endif %}\n),\n\nfx_rates AS (\n    SELECT * FROM {{ ref('int_fx_normalized_rates') }}\n),\n\nmonthly_aggregates AS (\n    SELECT\n        TO_CHAR(d.event_date, 'YYYY-MM') AS month_id,\n        SUM(d.gross_amount_usd) AS gross_revenue,\n        SUM(d.annualized_contract_val) AS net_arr,\n        SUM(CASE WHEN d.is_churned THEN d.mrr_impact ELSE 0 END) AS churned_mrr,\n        COUNT(DISTINCT d.customer_account_id) AS active_subscriptions\n    FROM daily_rev d\n    LEFT JOIN fx_rates fx ON d.currency = fx.currency AND d.event_date = fx.rate_date\n    GROUP BY 1\n)\n\nSELECT\n    month_id,\n    ROUND(gross_revenue, 2) AS gross_revenue,\n    ROUND(net_arr, 2) AS net_arr,\n    ROUND(churned_mrr, 2) AS churned_mrr,\n    active_subscriptions\nFROM monthly_aggregates\nORDER BY month_id DESC;`}\n
\n
\n
\n \n\n {/* TAB 4: SLA & Governance */}\n \n
\n {/* Ownership & Contacts */}\n \n
\n \n

Table Ownership & Escalation

\n
\n
\n
\n Owning Team:\n Data Platform Squad\n
\n
\n Technical Lead:\n @sarah.lin (Principal Data Eng)\n
\n
\n Slack Channel:\n \n #data-platform-alerts\n \n
\n
\n On-Call Rotation:\n PagerDuty · Tier 1 High Severity\n
\n
\n \n\n {/* Freshness SLA Target */}\n \n
\n \n

Freshness SLA & Cadence

\n
\n
\n
\n Target SLA Threshold:\n < 60 minutes\n
\n
\n Current Latency:\n 18 minutes (Compliant)\n
\n
\n Cron Schedule:\n \n 0 * * * * (Hourly)\n \n
\n
\n Historical Uptime:\n 99.98% (Last 90 days)\n
\n
\n \n
\n \n \n \n )\n}\n", "type": "registry:page", "target": "~/components/blocks/DataCatalogLineage.tsx" } ], "dependencies": [ "lucide-react" ], "devDependencies": [], "registryDependencies": [ "https://uipkge.dev/r/react/badge.json", "https://uipkge.dev/r/react/button.json", "https://uipkge.dev/r/react/card.json", "https://uipkge.dev/r/react/input.json", "https://uipkge.dev/r/react/separator.json", "https://uipkge.dev/r/react/table.json", "https://uipkge.dev/r/react/tabs.json" ], "description": "Alation and Monte Carlo style enterprise data catalog lineage map and table documentation explorer with interactive upstream source DAGs, dbt transform models, downstream BI consumer traces, schema column definitions with types and constraints, automated data quality assertions, and SQL console query runner.", "categories": [ "devops", "app", "analytics", "data" ] }