{ "$schema": "https://uipkge.dev/schema/registry-item.json", "name": "etl-pipeline-dag", "title": "Etl Pipeline Dag", "type": "registry:page", "files": [ { "path": "packages/registry-angular/blocks/etl-pipeline-dag/etl-pipeline-dag.component.ts", "content": "// Airflow / Dagster style directed acyclic graph (DAG) pipeline visualizer: pipeline header\n// with trigger + export actions, four health metric cards, a five-stage execution topology\n// with selectable task nodes, and the per-node log inspector. Port of the React block 1:1.\nimport { ChangeDetectionStrategy, Component, Input, computed, signal } from '@angular/core'\nimport {\n ArrowRight,\n Check,\n CheckCircle2,\n Clock,\n Cpu,\n CreditCard,\n Database,\n FileDown,\n GitBranch,\n Globe,\n Layers,\n Loader2,\n LucideAngularModule,\n Play,\n ShieldCheck,\n Workflow,\n Zap,\n type LucideIconData,\n} from 'lucide-angular'\nimport { cn } from '@/lib/utils'\nimport { UiBadgeComponent } from '@/ui/badge/badge.component'\nimport { UiButtonComponent } from '@/ui/button/button.component'\nimport {\n UiCardComponent,\n UiCardContentComponent,\n UiCardDescriptionComponent,\n UiCardHeaderComponent,\n UiCardTitleComponent,\n} from '@/ui/card/card.component'\nimport { UiProgressComponent } from '@/ui/progress/progress.component'\nimport { UiEtlNodeDetailComponent } from './etl-node-detail.component'\nimport { pipelineNodes, type DagNode, type NodeCategory } from './etl-pipeline-dag-data'\n\nexport type { DagNode, NodeCategory }\n\ninterface NodeCardMeta {\n id: string\n icon: LucideIconData\n iconBoxClass: string\n subtitle: string\n footerTarget: string\n footerDone?: boolean\n chips: { label: string; toneClass: string }[]\n durationLabel: string\n centered?: boolean\n}\n\nconst NODE_CARDS: NodeCardMeta[] = [\n {\n id: 'extract_stripe_charges',\n icon: CreditCard,\n iconBoxClass: 'border-info/30 bg-info/10 text-info',\n subtitle: '148.3k records · 42.6 MB',\n footerTarget: 'raw_stripe.charges',\n chips: [],\n durationLabel: '18s',\n },\n {\n id: 'extract_currency_rates',\n icon: Globe,\n iconBoxClass: 'border-info/30 bg-info/10 text-info',\n subtitle: '34 FX pairs · 128 KB',\n footerTarget: 'raw_currency.rates',\n chips: [],\n durationLabel: '4s',\n },\n {\n id: 'transform_normalize_fx',\n icon: Cpu,\n iconBoxClass: 'border-chart-1/30 bg-chart-1/10 text-chart-1',\n subtitle: '148,290 rows · PySpark 3.5',\n footerTarget: 'stg_payments_fx',\n chips: [\n { label: '2 Upstreams', toneClass: 'border-border bg-muted/60 text-muted-foreground' },\n { label: 'Snappy', toneClass: 'border-border bg-muted/60 text-muted-foreground' },\n ],\n durationLabel: '42s',\n centered: true,\n },\n {\n id: 'validate_schema_anomalies',\n icon: ShieldCheck,\n iconBoxClass: 'border-warning/30 bg-warning/10 text-warning',\n subtitle: '18/18 checks passed (100%)',\n footerTarget: 'anomalies_audit',\n chips: [\n { label: '0 Anomalies', toneClass: 'border-success/30 bg-success/10 text-success' },\n { label: 'Z-Score 3σ', toneClass: 'border-border bg-muted/60 text-muted-foreground' },\n ],\n durationLabel: '14s',\n centered: true,\n },\n {\n id: 'load_snowflake_warehouse',\n icon: Database,\n iconBoxClass: 'border-cyan-500/30 bg-cyan-500/10 text-cyan-500',\n subtitle: '148,290 rows merged',\n footerTarget: 'fct_stripe_charges',\n chips: [\n { label: '4X-Large', toneClass: 'border-border bg-muted/60 text-muted-foreground' },\n { label: 'Clustered', toneClass: 'border-border bg-muted/60 text-muted-foreground' },\n ],\n durationLabel: '1m 20s',\n centered: true,\n },\n {\n id: 'refresh_dbt_analytics_marts',\n icon: Layers,\n iconBoxClass: 'border-success/30 bg-success/10 text-success',\n subtitle: '12 models · 48 tests',\n footerTarget: 'mart_finance_revenue',\n footerDone: true,\n chips: [\n { label: '8 Threads', toneClass: 'border-border bg-muted/60 text-muted-foreground' },\n { label: 'BI Synced', toneClass: 'border-success/30 bg-success/10 text-success' },\n ],\n durationLabel: '1m 40s',\n centered: true,\n },\n]\n\ninterface DagStage {\n heading: string\n badge: string\n badgeVariant: 'outline' | 'secondary'\n nodeIds: string[]\n}\n\nconst DAG_STAGES: DagStage[] = [\n {\n heading: '01 · Extract (2)',\n badge: 'Parallel',\n badgeVariant: 'outline',\n nodeIds: ['extract_stripe_charges', 'extract_currency_rates'],\n },\n { heading: '02 · Transform', badge: 'Join 2→1', badgeVariant: 'secondary', nodeIds: ['transform_normalize_fx'] },\n { heading: '03 · Quality', badge: '18 Checks', badgeVariant: 'outline', nodeIds: ['validate_schema_anomalies'] },\n { heading: '04 · Load', badge: 'Snowflake', badgeVariant: 'outline', nodeIds: ['load_snowflake_warehouse'] },\n { heading: '05 · Marts', badge: 'dbt Core', badgeVariant: 'outline', nodeIds: ['refresh_dbt_analytics_marts'] },\n]\n\nfunction getNodeCategoryBadgeClass(category: NodeCategory): string {\n switch (category) {\n case 'ingest':\n return 'border-info/30 bg-info/10 text-info'\n case 'transform':\n return 'border-chart-1/30 bg-chart-1/10 text-chart-1'\n case 'validate':\n return 'border-warning/30 bg-warning/10 text-warning'\n case 'load':\n return 'border-chart-2/30 bg-chart-2/10 text-chart-2'\n case 'model':\n return 'border-success/30 bg-success/10 text-success'\n default:\n return 'border-border bg-muted text-muted-foreground'\n }\n}\n\n@Component({\n changeDetection: ChangeDetectionStrategy.Eager,\n selector: 'ui-etl-pipeline-dag, [ui-etl-pipeline-dag]',\n standalone: true,\n // React renders the root
itself: the host stays out of layout and `class` goes to the root.\n host: { '[attr.class]': '\"contents\"' },\n imports: [\n LucideAngularModule,\n UiBadgeComponent,\n UiButtonComponent,\n UiCardComponent,\n UiCardContentComponent,\n UiCardDescriptionComponent,\n UiCardHeaderComponent,\n UiCardTitleComponent,\n UiProgressComponent,\n UiEtlNodeDetailComponent,\n ],\n template: `\n
\n \n
\n
\n \n
\n \n \n
\n\n
\n
\n

\n stripe_payments_daily_etl\n

\n v2.4.1\n
\n\n
\n \n \n \n 0 2 * * * · Daily at 02:00 UTC\n \n\n \n \n \n State: Success · Run ID: #run_90412\n \n\n \n @if (triggerSuccessToast()) {\n \n \n Triggered Run #run_90413 queued!\n \n }\n
\n
\n
\n\n \n
\n \n @if (copiedAllLogs()) {\n \n } @else {\n \n }\n {{ copiedAllLogs() ? 'Run Log Copied!' : 'Export Run Log' }}\n \n\n \n @if (isTriggering()) {\n \n } @else {\n \n }\n {{ isTriggering() ? 'Triggering DAG Run...' : 'Trigger DAG Run' }}\n \n
\n
\n
\n\n \n
\n \n
\n
\n
\n Total Duration\n \n \n
\n
\n
\n
04m:18s
\n
\n On schedule\n -14s vs 7d avg\n
\n
\n
\n
\n\n \n
\n
\n
\n Tasks Completed\n \n \n
\n
\n
\n
\n 8 / 8 tasks succeeded\n
\n
\n \n

100% completion · 0 retries · 0 failed

\n
\n
\n
\n
\n\n \n
\n
\n
\n Records Ingested\n \n \n
\n
\n
\n
\n 148,290 records\n
\n
\n 100% data fidelity\n 0 dropped rows\n
\n
\n
\n \n\n \n
\n
\n
\n Data Freshness\n \n \n
\n
\n
\n
\n Lag: 12m · SLA < 1h\n
\n
\n SLA Healthy\n Target: < 60m sync\n
\n
\n
\n \n \n\n \n
\n
\n
\n
\n
\n

DAG Execution Topology

\n 6 Connected Tasks\n
\n

\n Directed acyclic graph with dependency streams. Select any node to inspect telemetry and execution logs.\n

\n
\n\n \n
\n \n \n All Upstreams Resolved\n
\n \n \n Topology: 2 Branches → Converged\n
\n
\n
\n \n\n
\n \n
\n \n
\n @for (stage of dagStages; track stage.heading) {\n
\n
\n \n {{ stage.heading }}\n \n {{ stage.badge }}\n
\n\n @for (meta of stageMetas(stage); track meta.id) {\n \n
\n
\n \n \n
\n \n {{ nodeOf(meta.id).categoryLabel }}\n \n
\n \n \n {{ meta.durationLabel }}\n \n
\n\n
\n
{{ meta.id }}
\n
{{ meta.subtitle }}
\n
\n\n @if (meta.chips.length > 0) {\n
\n @for (chip of meta.chips; track chip.label) {\n \n {{ chip.label }}\n \n }\n
\n }\n\n
\n {{ meta.footerTarget }}\n @if (meta.footerDone) {\n \n } @else {\n \n }\n
\n
\n }\n
\n }\n
\n \n \n \n\n \n \n \n `,\n})\nexport class UiEtlPipelineDagComponent {\n protected readonly ArrowRight = ArrowRight\n protected readonly Check = Check\n protected readonly CheckCircle2 = CheckCircle2\n protected readonly Clock = Clock\n protected readonly Database = Database\n protected readonly FileDown = FileDown\n protected readonly GitBranch = GitBranch\n protected readonly Loader2 = Loader2\n protected readonly Play = Play\n protected readonly Workflow = Workflow\n protected readonly Zap = Zap\n protected readonly cn = cn\n\n @Input() set initialNodeId(v: string | undefined) {\n this.selectedNodeId.set(v ?? 'extract_stripe_charges')\n }\n @Input('class') className?: string\n\n readonly selectedNodeId = signal('extract_stripe_charges')\n readonly copiedAllLogs = signal(false)\n readonly isTriggering = signal(false)\n readonly triggerSuccessToast = signal(false)\n private copyTimer: ReturnType | undefined\n private triggerTimers: ReturnType[] = []\n\n protected readonly dagStages = DAG_STAGES\n private readonly nodeCards = NODE_CARDS\n private readonly nodes: DagNode[] = pipelineNodes\n\n readonly selectedNode = computed(() => {\n return this.nodes.find((n) => n.id === this.selectedNodeId()) ?? this.nodes[0]!\n })\n\n get rootClass(): string {\n return cn('w-full space-y-6', this.className)\n }\n\n nodeOf(id: string): DagNode {\n return this.nodes.find((n) => n.id === id) ?? this.nodes[0]!\n }\n\n stageMetas(stage: DagStage): NodeCardMeta[] {\n return stage.nodeIds.map((id) => this.nodeCards.find((m) => m.id === id)).filter((m): m is NodeCardMeta => !!m)\n }\n\n categoryClass(category: NodeCategory): string {\n return getNodeCategoryBadgeClass(category)\n }\n\n nodeIcon(id: string): LucideIconData {\n switch (id) {\n case 'extract_stripe_charges':\n return CreditCard\n case 'extract_currency_rates':\n return Globe\n case 'transform_normalize_fx':\n return Cpu\n case 'validate_schema_anomalies':\n return ShieldCheck\n case 'load_snowflake_warehouse':\n return Database\n case 'refresh_dbt_analytics_marts':\n return Layers\n default:\n return Workflow\n }\n }\n\n nodeCardClass(meta: NodeCardMeta): string {\n return cn(\n 'group focus-visible:ring-ring relative cursor-pointer rounded-xl border p-3.5 transition-colors duration-200 focus-visible:ring-2 focus-visible:outline-none',\n meta.centered && 'my-auto',\n this.selectedNodeId() === meta.id\n ? 'border-primary bg-primary/[0.04] ring-primary/30 shadow-xs ring-2'\n : 'border-border bg-card hover:border-primary/50 hover:bg-muted/30',\n )\n }\n\n triggerDag(): void {\n if (this.isTriggering()) return\n this.isTriggering.set(true)\n this.triggerSuccessToast.set(false)\n this.triggerTimers.push(\n setTimeout(() => {\n this.isTriggering.set(false)\n this.triggerSuccessToast.set(true)\n this.triggerTimers.push(\n setTimeout(() => {\n this.triggerSuccessToast.set(false)\n }, 4000),\n )\n }, 1600),\n )\n }\n\n async exportFullRunLog(): Promise {\n const fullLog = this.nodes\n .map((node) => {\n const header = `=== Task: ${node.name} (${node.operator}) - Duration: ${node.duration} ===`\n const body = node.logs.map((l) => `[${l.timestamp}] [${l.level}] [${l.source}] ${l.message}`).join('\\n')\n return `${header}\\n${body}`\n })\n .join('\\n\\n')\n try {\n await navigator.clipboard.writeText(fullLog)\n this.copiedAllLogs.set(true)\n clearTimeout(this.copyTimer)\n this.copyTimer = setTimeout(() => {\n this.copiedAllLogs.set(false)\n }, 2500)\n } catch {\n // Clipboard fallback\n }\n }\n}\n", "type": "registry:page", "target": "~/src/app/components/blocks/etl-pipeline-dag/etl-pipeline-dag.component.ts" }, { "path": "packages/registry-angular/blocks/etl-pipeline-dag/etl-node-detail.component.ts", "content": "// Per-node execution inspector: stdout log terminal with level filters + search, task\n// parameter manifest, upstream/downstream lineage with schema table, and quality assertions.\n// Port of the React EtlNodeDetail block 1:1.\nimport { ChangeDetectionStrategy, Component, EventEmitter, Input, Output, computed, signal } from '@angular/core'\nimport {\n Check,\n Code2,\n Copy,\n CornerDownRight,\n GitBranch,\n GitCommit,\n LucideAngularModule,\n Search,\n ShieldCheck,\n Terminal,\n X,\n type LucideIconData,\n} from 'lucide-angular'\nimport { cn } from '@/lib/utils'\nimport { UiBadgeComponent } from '@/ui/badge/badge.component'\nimport { UiButtonComponent } from '@/ui/button/button.component'\nimport { UiCardComponent, UiCardContentComponent, UiCardHeaderComponent } from '@/ui/card/card.component'\nimport {\n UiTabsComponent,\n UiTabsContentComponent,\n UiTabsListComponent,\n UiTabsTriggerComponent,\n} from '@/ui/tabs/tabs.component'\nimport type { DagNode, LogLine } from './etl-pipeline-dag-data'\n\nconst LOG_LEVELS = ['ALL', 'INFO', 'WARN', 'SQL', 'SUCCESS'] as const\nexport type LogLevelFilter = (typeof LOG_LEVELS)[number]\n\n@Component({\n changeDetection: ChangeDetectionStrategy.Eager,\n selector: 'ui-etl-node-detail, [ui-etl-node-detail]',\n standalone: true,\n // The block renders its own root card: keep this wrapper out of the layout.\n host: { '[attr.class]': '\"contents\"' },\n imports: [\n LucideAngularModule,\n UiBadgeComponent,\n UiButtonComponent,\n UiCardComponent,\n UiCardContentComponent,\n UiCardHeaderComponent,\n UiTabsComponent,\n UiTabsContentComponent,\n UiTabsListComponent,\n UiTabsTriggerComponent,\n ],\n template: `\n
\n
\n
\n \n
\n
\n \n \n
\n {{ node.name }}\n {{ node.categoryLabel }}\n \n \n Success\n \n
\n\n \n
\n Operator: {{ node.operator }}\n •\n Duration: {{ node.duration }}\n •\n Retries: {{ node.retries }}\n •\n RAM: {{ node.memoryPeak }}\n •\n CPU: {{ node.cpuPeak }}\n
\n
\n\n \n
\n \n
\n \n \n @if (logSearchQuery()) {\n \n \n \n }\n
\n\n \n \n\n \n \n @if (copiedLogs()) {\n \n } @else {\n \n }\n {{ copiedLogs() ? 'Logs Copied' : 'Copy Logs' }}\n \n
\n
\n
\n\n
\n \n \n \n \n \n \n \n\n \n \n \n
\n
\n Level:\n @for (lvl of logLevels; track lvl) {\n \n }\n
\n\n
\n Showing {{ filteredLogs().length }} of {{ node.logs.length }} lines\n
\n
\n\n \n
\n \n
\n
\n
\n \n \n \n
\n stdout · {{ node.operator }}\n
\n\n
\n host: runner-us-east-1a\n ·\n exit: 0 (success)\n
\n
\n\n \n
\n @if (filteredLogs().length === 0) {\n
\n No log entries matching current search or level filters.\n
\n } @else {\n @for (log of filteredLogs(); track $index) {\n
\n \n \n {{ $index + 1 }}\n \n\n \n [{{ log.timestamp }}]\n\n \n [{{ log.level }}]\n\n \n [{{ log.source }}]\n\n \n {{ log.message }}\n
\n }\n }\n
\n
\n \n\n \n \n
\n
\n Task Configuration Manifest ({{ node.operator }})\n
\n
\n @for (entry of configEntries(); track entry[0]) {\n
\n {{ entry[0] }}\n \n {{ entry[1] }}\n \n
\n }\n
\n
\n \n\n \n \n
\n \n
\n
\n \n Upstream Dependencies ({{ node.upstream.length }})\n
\n @if (node.upstream.length === 0) {\n
\n Root extraction node. No upstream parent dependencies.\n
\n } @else {\n
\n @for (up of node.upstream; track up) {\n \n {{ up }}\n Resolved\n
\n }\n
\n }\n
\n\n \n
\n
\n \n Downstream Consumers ({{ node.downstream.length }})\n
\n @if (node.downstream.length === 0) {\n
\n Terminal node. Emits finalized reporting dataset.\n
\n } @else {\n
\n @for (down of node.downstream; track down) {\n \n {{ down }}\n Triggered\n
\n }\n
\n }\n
\n \n\n \n
\n
\n Target Dataset Schema ({{ node.target }})\n
\n
\n \n \n \n \n \n \n \n \n \n \n @for (col of node.schemaColumns; track col.name) {\n \n \n \n \n \n \n }\n \n
Column NameData TypeNullableDescription
{{ col.name }}{{ col.type }}{{ col.nullable ? 'YES' : 'NO' }}{{ col.description }}
\n
\n
\n \n\n \n \n
\n
\n Great Expectations / Assertion Test Suite\n {{ passedChecks() }} / {{ node.qualityChecks.length }} PASSED\n
\n
\n @for (check of node.qualityChecks; track check.id) {\n
\n
\n
\n PASSED\n {{ check.assertion }}\n
\n

\n Target column: {{ check.column }}\n

\n
\n\n
\n
\n Observed:\n {{ check.observed }}\n
\n
\n Threshold:\n {{ check.threshold }}\n
\n
\n
\n }\n
\n
\n \n \n \n \n `,\n})\nexport class UiEtlNodeDetailComponent {\n protected readonly Check = Check\n protected readonly Code2 = Code2\n protected readonly Copy = Copy\n protected readonly CornerDownRight = CornerDownRight\n protected readonly GitBranch = GitBranch\n protected readonly GitCommit = GitCommit\n protected readonly Search = Search\n protected readonly ShieldCheck = ShieldCheck\n protected readonly Terminal = Terminal\n protected readonly X = X\n protected readonly cn = cn\n protected readonly logLevels = LOG_LEVELS\n\n @Input({ required: true }) set node(v: DagNode) {\n this.nodeState.set(v)\n }\n get node(): DagNode {\n return this.nodeState()!\n }\n @Input() categoryBadgeClass = ''\n @Input() icon: LucideIconData | null = null\n @Output() selectNode = new EventEmitter()\n\n readonly nodeState = signal(null)\n\n readonly activeTab = signal('logs')\n readonly logSearchQuery = signal('')\n readonly selectedLogLevel = signal('ALL')\n readonly isWrapped = signal(false)\n readonly copiedLogs = signal(false)\n private copyTimer: ReturnType | undefined\n\n readonly filteredLogs = computed(() => {\n const node = this.nodeState()\n if (!node) return []\n const query = this.logSearchQuery().trim().toLowerCase()\n const level = this.selectedLogLevel()\n return node.logs.filter((log) => {\n const matchesLevel = level === 'ALL' || log.level === level\n const matchesQuery =\n query === '' ||\n log.message.toLowerCase().includes(query) ||\n log.source.toLowerCase().includes(query) ||\n log.timestamp.toLowerCase().includes(query)\n return matchesLevel && matchesQuery\n })\n })\n\n readonly passedChecks = computed(\n () => (this.nodeState()?.qualityChecks ?? []).filter((c) => c.status === 'passed').length,\n )\n\n readonly configEntries = computed<[string, string][]>(() => Object.entries(this.nodeState()?.configParams ?? {}))\n\n levelButtonClass(lvl: LogLevelFilter): string {\n return cn(\n 'min-h-6 rounded-md px-2 py-0.5 font-mono text-xs transition-colors',\n this.selectedLogLevel() === lvl\n ? 'bg-primary text-primary-foreground font-semibold'\n : 'border-border bg-muted/30 text-muted-foreground hover:text-foreground border',\n )\n }\n\n logLineClass(): string {\n return cn(\n '-mx-1 flex items-start rounded px-1 py-0.5 hover:bg-zinc-900/60',\n this.isWrapped() ? 'break-all whitespace-pre-wrap' : 'overflow-x-auto whitespace-pre',\n )\n }\n\n logLevelClass(log: LogLine): string {\n return cn(\n 'mr-2 shrink-0 font-semibold',\n log.level === 'INFO' && 'text-info',\n log.level === 'WARN' && 'text-warning',\n log.level === 'SQL' && 'text-chart-1',\n log.level === 'SUCCESS' && 'text-success font-bold',\n log.level === 'ERROR' && 'text-destructive font-bold',\n )\n }\n\n toggleWrap(): void {\n this.isWrapped.update((v) => !v)\n }\n\n async copyNodeLogs(): Promise {\n const node = this.nodeState()\n if (!node) return\n const text = node.logs.map((l) => `[${l.timestamp}] [${l.level}] [${l.source}] ${l.message}`).join('\\n')\n try {\n await navigator.clipboard.writeText(text)\n this.copiedLogs.set(true)\n clearTimeout(this.copyTimer)\n this.copyTimer = setTimeout(() => this.copiedLogs.set(false), 2000)\n } catch {\n // Clipboard fallback\n }\n }\n}\n", "type": "registry:page", "target": "~/src/app/components/blocks/etl-pipeline-dag/etl-node-detail.component.ts" }, { "path": "packages/registry-angular/blocks/etl-pipeline-dag/etl-pipeline-dag-data.ts", "content": "// Shared DAG types + sample pipeline telemetry. Port of the React block data 1:1:\n// same six tasks, operators, durations, quality checks, schemas, and stdout logs.\nexport type NodeStatus = 'success' | 'running' | 'failed' | 'queued'\nexport type NodeCategory = 'ingest' | 'transform' | 'validate' | 'load' | 'model'\n\nexport interface QualityCheck {\n id: string\n assertion: string\n column: string\n status: 'passed' | 'warning' | 'failed'\n observed: string\n threshold: string\n}\n\nexport interface SchemaColumn {\n name: string\n type: string\n nullable: boolean\n description: string\n}\n\nexport interface LogLine {\n timestamp: string\n level: 'INFO' | 'WARN' | 'SQL' | 'SUCCESS' | 'ERROR'\n source: string\n message: string\n}\n\nexport interface DagNode {\n id: string\n name: string\n category: NodeCategory\n categoryLabel: string\n stageNumber: number\n stageTitle: string\n operator: string\n status: NodeStatus\n duration: string\n durationSec: number\n records: string\n bytes: string\n startedAt: string\n finishedAt: string\n target: string\n engine: string\n upstream: string[]\n downstream: string[]\n retries: string\n memoryPeak: string\n cpuPeak: string\n configParams: Record\n qualityChecks: QualityCheck[]\n schemaColumns: SchemaColumn[]\n logs: LogLine[]\n}\n\nexport const pipelineNodes: DagNode[] = [\n {\n id: 'extract_stripe_charges',\n name: 'extract_stripe_charges',\n category: 'ingest',\n categoryLabel: 'INGEST',\n stageNumber: 1,\n stageTitle: 'Extract & Ingest',\n operator: 'StripeApiConnector',\n status: 'success',\n duration: '18s',\n durationSec: 18.2,\n records: '148,290 records',\n bytes: '42.6 MB',\n startedAt: '02:00:02 UTC',\n finishedAt: '02:00:20 UTC',\n target: 'raw_stripe.charges',\n engine: 'Airbyte / Stripe REST API v2024-06',\n upstream: [],\n downstream: ['transform_normalize_fx'],\n retries: '0 / 3',\n memoryPeak: '342 MB',\n cpuPeak: '18%',\n configParams: {\n endpoint: 'https://api.stripe.com/v1/charges',\n batch_size: '10,000',\n pagination_mode: 'starting_after cursor',\n target_s3_bucket: 's3://lakehouse-raw/stripe/charges/dt=2026-02-21/',\n compression_codec: 'snappy (parquet)',\n iam_role_arn: 'arn:aws:iam::12498214:role/etl-stripe-ingestion',\n secret_vault_ref: 'vault://production/credentials/stripe_api_key',\n },\n qualityChecks: [\n {\n id: 'q1',\n assertion: 'HTTP 200 Response Ratio',\n column: 'api_response',\n status: 'passed',\n observed: '100% (15/15 chunks)',\n threshold: '100%',\n },\n {\n id: 'q2',\n assertion: 'Payload Schema Checksum',\n column: 'raw_payload',\n status: 'passed',\n observed: 'sha256:8f4c91...',\n threshold: 'Valid JSON',\n },\n {\n id: 'q3',\n assertion: 'Record Count Lower Bound',\n column: 'id',\n status: 'passed',\n observed: '148,290 records',\n threshold: '> 50,000',\n },\n ],\n schemaColumns: [\n {\n name: 'id',\n type: 'VARCHAR(64)',\n nullable: false,\n description: 'Stripe unique charge identifier (ch_xxx)',\n },\n {\n name: 'amount',\n type: 'BIGINT',\n nullable: false,\n description: 'Charge amount in smallest currency unit (cents)',\n },\n {\n name: 'currency',\n type: 'VARCHAR(3)',\n nullable: false,\n description: 'Three-letter ISO currency code (USD, EUR, GBP)',\n },\n {\n name: 'customer_id',\n type: 'VARCHAR(64)',\n nullable: true,\n description: 'Associated customer reference (cus_xxx)',\n },\n {\n name: 'status',\n type: 'VARCHAR(32)',\n nullable: false,\n description: 'Charge status (succeeded, pending, failed)',\n },\n {\n name: 'created_at',\n type: 'TIMESTAMP_TZ',\n nullable: false,\n description: 'Stripe transaction creation epoch timestamp',\n },\n ],\n logs: [\n {\n timestamp: '02:00:02.104',\n level: 'INFO',\n source: 'worker_4',\n message: 'Initializing Stripe extraction worker on task runner runner-us-east-1a',\n },\n {\n timestamp: '02:00:02.482',\n level: 'INFO',\n source: 'auth',\n message: 'Authenticated with KMS secret token: sec_live_stripe_vault_****',\n },\n {\n timestamp: '02:00:03.119',\n level: 'INFO',\n source: 'query',\n message: 'Requesting charges filter: created >= 1708473600 (2026-02-21 00:00:00 UTC)',\n },\n {\n timestamp: '02:00:05.892',\n level: 'INFO',\n source: 'stream',\n message: 'Chunk 1/15 fetched: 10,000 records (HTTP 200 OK - 2.4s latency)',\n },\n {\n timestamp: '02:00:09.140',\n level: 'INFO',\n source: 'stream',\n message: 'Chunk 5/15 fetched: 50,000 records (HTTP 200 OK - 1.8s latency)',\n },\n {\n timestamp: '02:00:14.730',\n level: 'INFO',\n source: 'stream',\n message: 'Chunk 11/15 fetched: 110,000 records (HTTP 200 OK - 1.9s latency)',\n },\n {\n timestamp: '02:00:18.915',\n level: 'INFO',\n source: 'stream',\n message: 'Chunk 15/15 fetched: 148,290 records (HTTP 200 OK - 1.1s latency)',\n },\n {\n timestamp: '02:00:19.420',\n level: 'INFO',\n source: 'parquet',\n message: 'Staged parquet buffer to s3://lakehouse-raw/stripe/charges/dt=2026-02-21/part-001.parquet',\n },\n {\n timestamp: '02:00:20.312',\n level: 'SUCCESS',\n source: 'task',\n message: 'Task extract_stripe_charges completed successfully in 18.208s. Checksum verified: sha256:8f4c91',\n },\n ],\n },\n {\n id: 'extract_currency_rates',\n name: 'extract_currency_rates',\n category: 'ingest',\n categoryLabel: 'INGEST',\n stageNumber: 1,\n stageTitle: 'Extract & Ingest',\n operator: 'EcbRatesConnector',\n status: 'success',\n duration: '4s',\n durationSec: 4.1,\n records: '34 FX pairs',\n bytes: '128 KB',\n startedAt: '02:00:02 UTC',\n finishedAt: '02:00:06 UTC',\n target: 'raw_currency.rates',\n engine: 'ECB SDMX API / FastHTTP',\n upstream: [],\n downstream: ['transform_normalize_fx'],\n retries: '0 / 3',\n memoryPeak: '84 MB',\n cpuPeak: '6%',\n configParams: {\n endpoint: 'https://data-api.ecb.europa.eu/service/data/EXR/D..EUR.SP00.A',\n benchmark_currency: 'USD',\n feed_type: 'Daily Official Reference Spot',\n output_format: 'JSON / Arrow Table',\n target_s3_bucket: 's3://lakehouse-raw/rates/dt=2026-02-21/',\n },\n qualityChecks: [\n {\n id: 'q1',\n assertion: 'Base Currency Coverage',\n column: 'currency_code',\n status: 'passed',\n observed: '34/34 major currencies',\n threshold: '>= 30',\n },\n {\n id: 'q2',\n assertion: 'EUR/USD Rate Sanity Check',\n column: 'spot_rate',\n status: 'passed',\n observed: '1.0842 USD',\n threshold: '0.80 - 1.40',\n },\n {\n id: 'q3',\n assertion: 'Zero Value Absence',\n column: 'rate_multiplier',\n status: 'passed',\n observed: '0 non-zero values',\n threshold: '0 zeros',\n },\n ],\n schemaColumns: [\n {\n name: 'currency_code',\n type: 'VARCHAR(3)',\n nullable: false,\n description: 'Three letter ISO code (EUR, GBP, JPY, CAD)',\n },\n {\n name: 'rate_to_usd',\n type: 'DECIMAL(12, 6)',\n nullable: false,\n description: 'Standardized multiplier to convert 1 unit to USD',\n },\n {\n name: 'published_date',\n type: 'DATE',\n nullable: false,\n description: 'Official publication date by Central Bank',\n },\n {\n name: 'ingested_at',\n type: 'TIMESTAMP_TZ',\n nullable: false,\n description: 'Ingestion pipeline timestamp',\n },\n ],\n logs: [\n {\n timestamp: '02:00:02.108',\n level: 'INFO',\n source: 'worker_1',\n message: 'Starting currency spot exchange rate ingestion from European Central Bank API',\n },\n {\n timestamp: '02:00:02.740',\n level: 'INFO',\n source: 'http',\n message: 'GET https://data-api.ecb.europa.eu/service/data/EXR/D..EUR.SP00.A?startPeriod=2026-02-20',\n },\n {\n timestamp: '02:00:03.921',\n level: 'INFO',\n source: 'parser',\n message: 'SDMX-ML XML payload decoded (34 reference base currencies parsed)',\n },\n {\n timestamp: '02:00:04.450',\n level: 'INFO',\n source: 'transform',\n message:\n 'Inverted base currency rates against USD benchmark (EUR/USD: 1.0842, GBP/USD: 1.2610, JPY/USD: 154.20)',\n },\n {\n timestamp: '02:00:05.620',\n level: 'INFO',\n source: 's3',\n message: 'Writing FX snapshots to s3://lakehouse-raw/rates/fx_daily_20260221.json',\n },\n {\n timestamp: '02:00:06.210',\n level: 'SUCCESS',\n source: 'task',\n message: 'Task extract_currency_rates completed in 4.102s. 34/34 currency pairs ingested.',\n },\n ],\n },\n {\n id: 'transform_normalize_fx',\n name: 'transform_normalize_fx',\n category: 'transform',\n categoryLabel: 'TRANSFORM',\n stageNumber: 2,\n stageTitle: 'Transform & Normalize',\n operator: 'PySparkBatchOperator',\n status: 'success',\n duration: '42s',\n durationSec: 42.6,\n records: '148,290 rows',\n bytes: '58.2 MB',\n startedAt: '02:00:21 UTC',\n finishedAt: '02:01:03 UTC',\n target: 'stg_payments_fx',\n engine: 'PySpark 3.5.1 / Apache Arrow 15.0',\n upstream: ['extract_stripe_charges', 'extract_currency_rates'],\n downstream: ['validate_schema_anomalies'],\n retries: '0 / 3',\n memoryPeak: '1,280 MB',\n cpuPeak: '74%',\n configParams: {\n spark_driver_memory: '4g',\n spark_executor_memory: '8g',\n executor_instances: '4',\n join_strategy: 'BroadcastHashJoin(stripe.currency = fx.currency_code)',\n precision_mode: 'DECIMAL(18, 4)',\n output_staging_path: 's3://lakehouse-stage/payments_fx/dt=2026-02-21/',\n },\n qualityChecks: [\n {\n id: 'q1',\n assertion: 'FX Broadcast Join Match Rate',\n column: 'currency',\n status: 'passed',\n observed: '100% matched (0 orphaned FX)',\n threshold: '100%',\n },\n {\n id: 'q2',\n assertion: 'Non-negative Amount USD',\n column: 'amount_usd',\n status: 'passed',\n observed: '0 negative rows',\n threshold: '0 negative',\n },\n {\n id: 'q3',\n assertion: 'Timestamp Timezone Normalization',\n column: 'created_at_utc',\n status: 'passed',\n observed: 'All UTC ISO-8601',\n threshold: 'Valid UTC',\n },\n ],\n schemaColumns: [\n {\n name: 'charge_id',\n type: 'VARCHAR(64)',\n nullable: false,\n description: 'Normalized unique charge identifier',\n },\n {\n name: 'customer_id',\n type: 'VARCHAR(64)',\n nullable: true,\n description: 'Customer identifier reference',\n },\n {\n name: 'original_amount',\n type: 'DECIMAL(18,2)',\n nullable: false,\n description: 'Amount in transaction original currency',\n },\n {\n name: 'original_currency',\n type: 'VARCHAR(3)',\n nullable: false,\n description: 'Original ISO transaction currency',\n },\n {\n name: 'fx_rate_applied',\n type: 'DECIMAL(12,6)',\n nullable: false,\n description: 'Exchange rate multiplier to USD',\n },\n {\n name: 'amount_usd',\n type: 'DECIMAL(18,4)',\n nullable: false,\n description: 'Standardized gross revenue in USD',\n },\n {\n name: 'net_interchange_fee_usd',\n type: 'DECIMAL(18,4)',\n nullable: false,\n description: 'Estimated payment processing fees in USD',\n },\n {\n name: 'created_at_utc',\n type: 'TIMESTAMP_TZ',\n nullable: false,\n description: 'UTC converted timestamp',\n },\n ],\n logs: [\n {\n timestamp: '02:00:21.050',\n level: 'INFO',\n source: 'spark',\n message: 'Initialized PySpark cluster (executor_count: 4, cores_per_exec: 4, memory: 8GB)',\n },\n {\n timestamp: '02:00:22.410',\n level: 'INFO',\n source: 'upstream',\n message: 'Joined dependencies: extract_stripe_charges (OK), extract_currency_rates (OK)',\n },\n {\n timestamp: '02:00:24.980',\n level: 'INFO',\n source: 'dag.plan',\n message: 'Logical plan: BroadcastHashJoin(stripe_charges.currency = fx_rates.currency_code)',\n },\n {\n timestamp: '02:00:30.120',\n level: 'INFO',\n source: 'spark.exec',\n message: 'Partition 0-7 completed: Normalized cents to standardized float amounts',\n },\n {\n timestamp: '02:00:37.450',\n level: 'INFO',\n source: 'spark.exec',\n message: 'Calculated converted_amount_usd, net_interchange_fee_usd, and tax_withheld_usd',\n },\n {\n timestamp: '02:00:48.890',\n level: 'INFO',\n source: 'spark.exec',\n message: 'Partition 8-15 completed: Applied ISO-8601 UTC timestamp timezone adjustments',\n },\n {\n timestamp: '02:00:59.320',\n level: 'INFO',\n source: 'io.write',\n message: 'Emitted staging dataset to s3://lakehouse-stage/payments_fx/dt=2026-02-21/ (148,290 records)',\n },\n {\n timestamp: '02:01:03.650',\n level: 'SUCCESS',\n source: 'task',\n message: 'Task transform_normalize_fx finished in 42.600s. 0 join drops, 100% matched FX rates.',\n },\n ],\n },\n {\n id: 'validate_schema_anomalies',\n name: 'validate_schema_anomalies',\n category: 'validate',\n categoryLabel: 'VALIDATE',\n stageNumber: 3,\n stageTitle: 'Quality & Audit',\n operator: 'GreatExpectationsOperator',\n status: 'success',\n duration: '14s',\n durationSec: 14.3,\n records: '18/18 checks',\n bytes: '1.2 MB',\n startedAt: '02:01:04 UTC',\n finishedAt: '02:01:18 UTC',\n target: 'anomalies_audit',\n engine: 'Great Expectations 0.18 + Monte Carlo',\n upstream: ['transform_normalize_fx'],\n downstream: ['load_snowflake_warehouse'],\n retries: '0 / 3',\n memoryPeak: '412 MB',\n cpuPeak: '28%',\n configParams: {\n suite_name: 'suite_stripe_payments_strict_v3',\n assertion_count: '18',\n anomaly_detection_model: 'Z-Score 3-Sigma Rolling Window (30-day baseline)',\n halt_pipeline_on_error: 'True',\n alerting_webhook: 'slack://#data-pipeline-alerts',\n },\n qualityChecks: [\n {\n id: 'q1',\n assertion: 'expect_column_values_to_not_be_null',\n column: 'charge_id',\n status: 'passed',\n observed: '148,290 / 148,290 (100%)',\n threshold: '100% non-null',\n },\n {\n id: 'q2',\n assertion: 'expect_column_values_to_be_unique',\n column: 'charge_id',\n status: 'passed',\n observed: '0 duplicate keys',\n threshold: '0 duplicates',\n },\n {\n id: 'q3',\n assertion: 'expect_column_values_to_be_in_set',\n column: 'status',\n status: 'passed',\n observed: 'succeeded, failed, refunded',\n threshold: 'Allowed set',\n },\n {\n id: 'q4',\n assertion: 'expect_column_values_to_be_between',\n column: 'amount_usd',\n status: 'passed',\n observed: '$0.50 - $24,900.00',\n threshold: '$0.50 - $250k',\n },\n {\n id: 'q5',\n assertion: 'expect_table_row_count_to_be_between',\n column: '*',\n status: 'passed',\n observed: '148,290 rows',\n threshold: '100,000 - 200,000',\n },\n {\n id: 'q6',\n assertion: 'expect_volume_drift_z_score_within_bound',\n column: 'daily_volume',\n status: 'passed',\n observed: 'z = +0.42 (Normal)',\n threshold: '|z| < 3.0',\n },\n ],\n schemaColumns: [\n {\n name: 'test_id',\n type: 'VARCHAR(64)',\n nullable: false,\n description: 'Unique assertion execution ID',\n },\n {\n name: 'expectation_type',\n type: 'VARCHAR(128)',\n nullable: false,\n description: 'Great Expectations rule name',\n },\n {\n name: 'target_column',\n type: 'VARCHAR(64)',\n nullable: true,\n description: 'Target column evaluated',\n },\n {\n name: 'result_status',\n type: 'VARCHAR(16)',\n nullable: false,\n description: 'PASS or FAIL status',\n },\n {\n name: 'observed_metrics',\n type: 'JSON',\n nullable: false,\n description: 'Computed profiling metrics summary',\n },\n ],\n logs: [\n {\n timestamp: '02:01:04.110',\n level: 'INFO',\n source: 'gx.suite',\n message: 'Loading expectation suite: suite_stripe_payments_strict_v3',\n },\n {\n timestamp: '02:01:05.420',\n level: 'INFO',\n source: 'assertion',\n message: '[1/18] expect_column_values_to_not_be_null(column=charge_id) -> PASSED (148,290/148,290 valid)',\n },\n {\n timestamp: '02:01:07.190',\n level: 'INFO',\n source: 'assertion',\n message: '[2/18] expect_column_values_to_be_unique(column=charge_id) -> PASSED (0 duplicate keys)',\n },\n {\n timestamp: '02:01:09.650',\n level: 'INFO',\n source: 'assertion',\n message: '[5/18] expect_column_values_to_be_in_set(column=status, set=[succeeded, failed, refunded]) -> PASSED',\n },\n {\n timestamp: '02:01:12.330',\n level: 'INFO',\n source: 'assertion',\n message:\n '[11/18] expect_column_values_to_be_between(column=amount_usd, min_value=0.50, max_value=250000.00) -> PASSED',\n },\n {\n timestamp: '02:01:15.840',\n level: 'INFO',\n source: 'assertion',\n message:\n '[18/18] expect_table_row_count_to_be_between(min_value=100000, max_value=200000) -> PASSED (148,290 rows)',\n },\n {\n timestamp: '02:01:17.200',\n level: 'INFO',\n source: 'anomaly',\n message:\n 'Statistical z-score drift test: Revenue mean +1.4% vs 30d rolling window (within normal range: ±3.0σ)',\n },\n {\n timestamp: '02:01:18.410',\n level: 'SUCCESS',\n source: 'task',\n message: 'Quality suite passed with 100% score (18/18 expectations verified, 0 anomalies detected).',\n },\n ],\n },\n {\n id: 'load_snowflake_warehouse',\n name: 'load_snowflake_warehouse',\n category: 'load',\n categoryLabel: 'LOAD',\n stageNumber: 4,\n stageTitle: 'Warehouse Load',\n operator: 'SnowflakeMergeOperator',\n status: 'success',\n duration: '1m 20s',\n durationSec: 80.0,\n records: '148,290 rows',\n bytes: '74.8 MB',\n startedAt: '02:01:19 UTC',\n finishedAt: '02:02:39 UTC',\n target: 'fct_stripe_charges',\n engine: 'Snowflake / TRANSFORMING_XL',\n upstream: ['validate_schema_anomalies'],\n downstream: ['refresh_dbt_analytics_marts'],\n retries: '0 / 3',\n memoryPeak: '520 MB',\n cpuPeak: '15%',\n configParams: {\n database: 'PROD_ANALYTICS',\n schema: 'PAYMENTS',\n target_table: 'ANALYTICS.PAYMENTS.FCT_STRIPE_CHARGES',\n warehouse_name: 'TRANSFORMING_XL (4X-Large)',\n merge_primary_key: 'charge_id',\n clustering_keys: '(created_at_utc::date, original_currency)',\n auto_suspend_seconds: '60',\n },\n qualityChecks: [\n {\n id: 'q1',\n assertion: 'Snowflake Transaction ACID Commit',\n column: 'txn_id',\n status: 'passed',\n observed: 'Committed #TXN-89021',\n threshold: 'Committed',\n },\n {\n id: 'q2',\n assertion: 'Merge Mutation Parity',\n column: 'rows_merged',\n status: 'passed',\n observed: '142.4k ins / 5.8k upd',\n threshold: '148,290 total',\n },\n {\n id: 'q3',\n assertion: 'Clustering Depth Efficiency',\n column: 'clustering_depth',\n status: 'passed',\n observed: 'Average depth: 1.12',\n threshold: '< 2.0',\n },\n ],\n schemaColumns: [\n {\n name: 'charge_id',\n type: 'VARCHAR(64)',\n nullable: false,\n description: 'Primary key charge hash',\n },\n {\n name: 'customer_id',\n type: 'VARCHAR(64)',\n nullable: true,\n description: 'Foreign key to DIM_CUSTOMERS',\n },\n {\n name: 'amount_usd',\n type: 'NUMBER(18,4)',\n nullable: false,\n description: 'Standardized gross charge amount in USD',\n },\n {\n name: 'fee_usd',\n type: 'NUMBER(18,4)',\n nullable: false,\n description: 'Interchange and payment gateway fees',\n },\n {\n name: 'net_usd',\n type: 'NUMBER(18,4)',\n nullable: false,\n description: 'Net deposited funds (amount_usd - fee_usd)',\n },\n {\n name: 'status',\n type: 'VARCHAR(32)',\n nullable: false,\n description: 'Current transactional settlement state',\n },\n {\n name: 'created_at_utc',\n type: 'TIMESTAMP_NTZ',\n nullable: false,\n description: 'Event timestamp',\n },\n {\n name: '_dbt_loaded_at',\n type: 'TIMESTAMP_NTZ',\n nullable: false,\n description: 'Warehouse load audit timestamp',\n },\n ],\n logs: [\n {\n timestamp: '02:01:19.040',\n level: 'INFO',\n source: 'snowflake',\n message: 'Establishing TLS 1.3 session to acct_finance.snowflakecomputing.com',\n },\n {\n timestamp: '02:01:21.320',\n level: 'INFO',\n source: 'warehouse',\n message: 'Resumed warehouse TRANSFORMING_XL (cluster_size: 4X-Large, auto_suspend: 60s)',\n },\n {\n timestamp: '02:01:24.890',\n level: 'SQL',\n source: 'query',\n message:\n 'MERGE INTO ANALYTICS.PAYMENTS.FCT_STRIPE_CHARGES AS target USING @STAGE_S3_PAYMENTS AS stage ON target.charge_id = stage.charge_id WHEN MATCHED THEN UPDATE SET target.status = stage.status WHEN NOT MATCHED THEN INSERT (charge_id, customer_id, amount_usd, fee_usd, net_usd, status, created_at_utc, _dbt_loaded_at) VALUES (stage.charge_id, stage.customer_id, stage.amount_usd, stage.net_interchange_fee_usd, stage.amount_usd - stage.net_interchange_fee_usd, stage.status, stage.created_at_utc, CURRENT_TIMESTAMP())',\n },\n {\n timestamp: '02:01:45.120',\n level: 'INFO',\n source: 'pruner',\n message: 'Partition pruning: 42 micro-partitions scanned out of 1,280 total (96.7% pruned)',\n },\n {\n timestamp: '02:02:12.780',\n level: 'INFO',\n source: 'stats',\n message: 'Merge stats: 142,400 rows inserted, 5,890 existing rows updated (disputes/refund status sync)',\n },\n {\n timestamp: '02:02:32.410',\n level: 'INFO',\n source: 'cluster',\n message: 'Automatic clustering key (created_at_utc::date, original_currency) re-balanced in background',\n },\n {\n timestamp: '02:02:38.100',\n level: 'INFO',\n source: 'warehouse',\n message: 'Auto-suspend timer armed for warehouse TRANSFORMING_XL',\n },\n {\n timestamp: '02:02:39.050',\n level: 'SUCCESS',\n source: 'task',\n message: 'Snowflake warehouse load completed successfully in 80.010s. Target table committed.',\n },\n ],\n },\n {\n id: 'refresh_dbt_analytics_marts',\n name: 'refresh_dbt_analytics_marts',\n category: 'model',\n categoryLabel: 'MODEL',\n stageNumber: 5,\n stageTitle: 'Analytics Marts',\n operator: 'DbtCloudRunOperator',\n status: 'success',\n duration: '1m 40s',\n durationSec: 100.0,\n records: '12 models',\n bytes: '112.4 MB',\n startedAt: '02:02:40 UTC',\n finishedAt: '02:04:20 UTC',\n target: 'mart_finance_revenue',\n engine: 'dbt Core 1.8.2 / Jinja',\n upstream: ['load_snowflake_warehouse'],\n downstream: [],\n retries: '0 / 3',\n memoryPeak: '890 MB',\n cpuPeak: '62%',\n configParams: {\n dbt_project: 'bi_marts',\n target_environment: 'prod_snowflake',\n threads: '8',\n models_selection: 'tag:finance_daily+ tag:revenue_marts+',\n manifest_version: 'dbt-core v1.8.2 (schema v12)',\n downstream_webhooks: 'Looker Cache Flush, Hex App Refresh, Metabase Sync',\n },\n qualityChecks: [\n {\n id: 'q1',\n assertion: 'dbt Schema & Custom Tests',\n column: 'all_marts',\n status: 'passed',\n observed: '48/48 tests passed (0 failures)',\n threshold: '100% pass',\n },\n {\n id: 'q2',\n assertion: 'Incremental Model Freshness',\n column: 'fct_daily_gross_revenue',\n status: 'passed',\n observed: 'Watermark: 2026-02-21 02:00 UTC',\n threshold: '< 3h',\n },\n {\n id: 'q3',\n assertion: 'BI Semantic Layer Sync',\n column: 'Looker/Hex',\n status: 'passed',\n observed: '3/3 endpoints acknowledged',\n threshold: 'All ACK',\n },\n ],\n schemaColumns: [\n {\n name: 'report_date',\n type: 'DATE',\n nullable: false,\n description: 'Reporting dimension day',\n },\n {\n name: 'gross_revenue_usd',\n type: 'DECIMAL(20,2)',\n nullable: false,\n description: 'Total daily revenue converted to USD',\n },\n {\n name: 'net_revenue_usd',\n type: 'DECIMAL(20,2)',\n nullable: false,\n description: 'Gross revenue minus fees and refunds',\n },\n {\n name: 'active_paying_customers',\n type: 'INTEGER',\n nullable: false,\n description: 'Distinct customer count for the day',\n },\n {\n name: 'dispute_rate_pct',\n type: 'DECIMAL(6,4)',\n nullable: false,\n description: 'Daily payment dispute percentage',\n },\n {\n name: 'mrr_impact_usd',\n type: 'DECIMAL(18,2)',\n nullable: false,\n description: 'MRR movement delta attribution',\n },\n ],\n logs: [\n {\n timestamp: '02:02:40.090',\n level: 'INFO',\n source: 'dbt',\n message: 'Found 12 models, 48 tests, 6 snapshots, 4 semantic metrics in project bi_marts',\n },\n {\n timestamp: '02:02:42.510',\n level: 'INFO',\n source: 'dbt',\n message: 'Concurrency: 8 threads across target database PROD_ANALYTICS',\n },\n {\n timestamp: '02:02:48.330',\n level: 'INFO',\n source: 'dbt',\n message: '1 of 12 START incremental model marts.fct_daily_gross_revenue ................ [RUN]',\n },\n {\n timestamp: '02:02:59.880',\n level: 'INFO',\n source: 'dbt',\n message: '1 of 12 OK created incremental model marts.fct_daily_gross_revenue ........... [SUCCESS 11.55s]',\n },\n {\n timestamp: '02:03:00.120',\n level: 'INFO',\n source: 'dbt',\n message: '2 of 12 START table model marts.dim_customer_ltv .............................. [RUN]',\n },\n {\n timestamp: '02:03:18.420',\n level: 'INFO',\n source: 'dbt',\n message: '2 of 12 OK created table model marts.dim_customer_ltv ......................... [SUCCESS 18.30s]',\n },\n {\n timestamp: '02:03:19.050',\n level: 'INFO',\n source: 'dbt',\n message: '3 of 12 START view model marts.finance_mrr_arr_summary ....................... [RUN]',\n },\n {\n timestamp: '02:03:26.700',\n level: 'INFO',\n source: 'dbt',\n message: '3 of 12 OK created view model marts.finance_mrr_arr_summary ................... [SUCCESS 7.65s]',\n },\n {\n timestamp: '02:03:27.100',\n level: 'INFO',\n source: 'dbt',\n message: '4 of 12 START incremental model marts.fct_payment_dispute_rates .............. [RUN]',\n },\n {\n timestamp: '02:03:45.920',\n level: 'INFO',\n source: 'dbt',\n message: '4 of 12 OK created incremental model marts.fct_payment_dispute_rates ........ [SUCCESS 18.82s]',\n },\n {\n timestamp: '02:03:46.300',\n level: 'INFO',\n source: 'dbt',\n message: '5 to 12 START remaining downstream aggregate semantic marts .................. [RUN]',\n },\n {\n timestamp: '02:04:12.180',\n level: 'INFO',\n source: 'dbt',\n message: 'Running 48 data integrity tests on rebuilt marts ............................ [PASS 48/48]',\n },\n {\n timestamp: '02:04:18.500',\n level: 'INFO',\n source: 'dbt',\n message: 'Generated docs manifest and catalog metadata to target/manifest.json',\n },\n {\n timestamp: '02:04:20.120',\n level: 'SUCCESS',\n source: 'dbt',\n message:\n 'Finished running 12 models, 48 tests in 100.03s. All marts synchronized with Looker/Hex BI semantic layer.',\n },\n ],\n },\n]\n", "type": "registry:page", "target": "~/src/app/components/blocks/etl-pipeline-dag/etl-pipeline-dag-data.ts" }, { "path": "packages/registry-angular/blocks/etl-pipeline-dag/index.ts", "content": "export { UiEtlPipelineDagComponent } from './etl-pipeline-dag.component'\nexport { UiEtlNodeDetailComponent } from './etl-node-detail.component'\nexport {\n pipelineNodes,\n type DagNode,\n type LogLine,\n type NodeCategory,\n type NodeStatus,\n type QualityCheck,\n type SchemaColumn,\n} from './etl-pipeline-dag-data'\n", "type": "registry:page", "target": "~/src/app/components/blocks/etl-pipeline-dag/index.ts" } ], "dependencies": [ "lucide-angular" ], "devDependencies": [], "registryDependencies": [ "https://uipkge.dev/r/angular/badge.json", "https://uipkge.dev/r/angular/button.json", "https://uipkge.dev/r/angular/card.json", "https://uipkge.dev/r/angular/progress.json", "https://uipkge.dev/r/angular/tabs.json" ], "description": "Airflow and Dagster style directed acyclic graph (DAG) pipeline visualizer with interactive node execution topologies, health metrics, and terminal stdout log inspector.", "categories": [ "devops", "app", "analytics" ] }