{ "$schema": "https://ui.shadcn.com/schema/registry-item.json", "name": "etl-pipeline-dag", "title": "Etl Pipeline Dag", "type": "registry:page", "files": [ { "path": "packages/registry-react/blocks/etl-pipeline-dag/EtlPipelineDag.tsx", "content": "'use client'\n\nimport * as React from 'react'\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 Play,\n ShieldCheck,\n Workflow,\n Zap,\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 { Progress } from '@/components/ui/progress'\nimport { EtlNodeDetail } from './EtlNodeDetail'\n\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\nconst 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\nfunction getNodeCategoryBadgeClass(category: NodeCategory) {\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\nfunction getNodeIcon(id: string) {\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\nexport interface EtlPipelineDagProps extends React.HTMLAttributes {\n initialNodeId?: string\n}\n\nexport function EtlPipelineDag({ initialNodeId = 'extract_stripe_charges', className, ...props }: EtlPipelineDagProps) {\n const [selectedNodeId, setSelectedNodeId] = React.useState(initialNodeId)\n const [copiedAllLogs, setCopiedAllLogs] = React.useState(false)\n const [isTriggering, setIsTriggering] = React.useState(false)\n const [triggerSuccessToast, setTriggerSuccessToast] = React.useState(false)\n\n const selectedNode = React.useMemo(() => {\n return pipelineNodes.find((n) => n.id === selectedNodeId) ?? pipelineNodes[0]\n }, [selectedNodeId])\n\n const handleTriggerDag = React.useCallback(() => {\n if (isTriggering) return\n setIsTriggering(true)\n setTriggerSuccessToast(false)\n\n setTimeout(() => {\n setIsTriggering(false)\n setTriggerSuccessToast(true)\n setTimeout(() => {\n setTriggerSuccessToast(false)\n }, 4000)\n }, 1600)\n }, [isTriggering])\n\n const handleExportFullRunLog = React.useCallback(async () => {\n const fullLog = pipelineNodes\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\n try {\n await navigator.clipboard.writeText(fullLog)\n setCopiedAllLogs(true)\n setTimeout(() => {\n setCopiedAllLogs(false)\n }, 2500)\n } catch {\n // Fallback\n }\n }, [])\n\n return (\n
\n {/* Top Pipeline Header Card */}\n \n \n {/* Left Title and DAG Badges */}\n
\n
\n \n
\n\n
\n
\n

\n stripe_payments_daily_etl\n

\n \n v2.4.1\n \n
\n\n
\n {/* DAG Schedule Badge */}\n \n \n 0 2 * * * · Daily at 02:00 UTC\n \n\n {/* State Badge */}\n \n \n State: Success · Run ID: #run_90412\n \n\n {/* Live Queue Toast */}\n {triggerSuccessToast && (\n \n \n Triggered Run #run_90413 queued!\n \n )}\n
\n
\n
\n\n {/* Right Action Buttons */}\n
\n \n {copiedAllLogs ? : }\n {copiedAllLogs ? 'Run Log Copied!' : 'Export Run Log'}\n \n\n \n {isTriggering ? (\n \n ) : (\n \n )}\n {isTriggering ? 'Triggering DAG Run...' : 'Trigger DAG Run'}\n \n
\n \n \n\n {/* 4 Pipeline Health Metric Cards */}\n
\n {/* Total Duration */}\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 {/* Tasks Completed */}\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 {/* Records Ingested */}\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 {/* Data Freshness */}\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 {/* Interactive DAG Node Pipeline Graph */}\n \n \n
\n
\n
\n DAG Execution Topology\n \n 6 Connected Tasks\n \n
\n \n Directed acyclic graph with dependency streams. Select any node to inspect telemetry and execution logs.\n \n
\n\n {/* Quick Topology Status Info */}\n
\n
\n \n All Upstreams Resolved\n
\n
\n \n Topology: 2 Branches → Converged\n
\n
\n
\n \n\n \n {/* DAG Canvas Box */}\n
\n {/* Desktop 5-Stage Topology Pipeline Layout */}\n
\n {/* Stage 1: Extract / Ingest (2 Parallel Sources) */}\n
\n
\n \n 01 · Extract (2)\n \n \n Parallel\n \n
\n\n {/* Node 1: extract_stripe_charges */}\n setSelectedNodeId('extract_stripe_charges')}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n setSelectedNodeId('extract_stripe_charges')\n }\n }}\n >\n
\n
\n
\n \n
\n INGEST\n
\n \n \n 18s\n \n
\n\n
\n
\n extract_stripe_charges\n
\n
148.3k records · 42.6 MB
\n
\n\n
\n raw_stripe.charges\n \n
\n
\n\n {/* Node 2: extract_currency_rates */}\n setSelectedNodeId('extract_currency_rates')}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n setSelectedNodeId('extract_currency_rates')\n }\n }}\n >\n
\n
\n
\n \n
\n INGEST\n
\n \n \n 4s\n \n
\n\n
\n
\n extract_currency_rates\n
\n
34 FX pairs · 128 KB
\n
\n\n
\n raw_currency.rates\n \n
\n
\n
\n\n {/* Stage 2: Transform (PySpark Normalization) */}\n
\n
\n \n 02 · Transform\n \n \n Join 2→1\n \n
\n\n {/* Node 3: transform_normalize_fx */}\n setSelectedNodeId('transform_normalize_fx')}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n setSelectedNodeId('transform_normalize_fx')\n }\n }}\n >\n
\n
\n
\n \n
\n \n TRANSFORM\n \n
\n \n \n 42s\n \n
\n\n
\n
\n transform_normalize_fx\n
\n
148,290 rows · PySpark 3.5
\n
\n\n
\n \n 2 Upstreams\n \n \n Snappy\n \n
\n\n
\n stg_payments_fx\n \n
\n
\n
\n\n {/* Stage 3: Quality & Validation (Great Expectations) */}\n
\n
\n \n 03 · Quality\n \n \n 18 Checks\n \n
\n\n {/* Node 4: validate_schema_anomalies */}\n setSelectedNodeId('validate_schema_anomalies')}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n setSelectedNodeId('validate_schema_anomalies')\n }\n }}\n >\n
\n
\n
\n \n
\n VALIDATE\n
\n \n \n 14s\n \n
\n\n
\n
\n validate_schema_anomalies\n
\n
18/18 checks passed (100%)
\n
\n\n
\n \n 0 Anomalies\n \n \n Z-Score 3σ\n \n
\n\n
\n anomalies_audit\n \n
\n
\n \n\n {/* Stage 4: Warehouse Load (Snowflake) */}\n
\n
\n \n 04 · Load\n \n \n Snowflake\n \n
\n\n {/* Node 5: load_snowflake_warehouse */}\n setSelectedNodeId('load_snowflake_warehouse')}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n setSelectedNodeId('load_snowflake_warehouse')\n }\n }}\n >\n
\n
\n
\n \n
\n LOAD\n
\n \n \n 1m 20s\n \n
\n\n
\n
\n load_snowflake_warehouse\n
\n
148,290 rows merged
\n
\n\n
\n \n 4X-Large\n \n \n Clustered\n \n
\n\n
\n fct_stripe_charges\n \n
\n
\n \n\n {/* Stage 5: Analytics Modeling (dbt Core) */}\n
\n
\n \n 05 · Marts\n \n \n dbt Core\n \n
\n\n {/* Node 6: refresh_dbt_analytics_marts */}\n setSelectedNodeId('refresh_dbt_analytics_marts')}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n setSelectedNodeId('refresh_dbt_analytics_marts')\n }\n }}\n >\n
\n
\n
\n \n
\n MODEL\n
\n \n \n 1m 40s\n \n
\n\n
\n
\n refresh_dbt_analytics_marts\n
\n
12 models · 48 tests
\n
\n\n
\n \n 8 Threads\n \n \n BI Synced\n \n
\n\n
\n mart_finance_revenue\n \n
\n
\n \n \n \n \n \n\n {/* Selected Node Log Inspector Panel */}\n \n \n )\n}\n\nexport default EtlPipelineDag\n", "type": "registry:page", "target": "~/components/blocks/EtlPipelineDag.tsx" }, { "path": "packages/registry-react/blocks/etl-pipeline-dag/EtlNodeDetail.tsx", "content": "'use client'\n\nimport * as React from 'react'\nimport {\n Check,\n Code2,\n Copy,\n CornerDownRight,\n GitBranch,\n GitCommit,\n Search,\n ShieldCheck,\n Terminal,\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, CardHeader } from '@/components/ui/card'\nimport { Tabs, TabsContent, TabsList, TabsTrigger } from '@/components/ui/tabs'\nimport type { DagNode } from './EtlPipelineDag'\n\nexport interface EtlNodeDetailProps {\n node: DagNode\n categoryBadgeClass: string\n icon: React.ComponentType<{ className?: string }>\n onSelectNode: (id: string) => void\n}\n\nexport function EtlNodeDetail({ node, categoryBadgeClass, icon: NodeIcon, onSelectNode }: EtlNodeDetailProps) {\n const [activeTab, setActiveTab] = React.useState('logs')\n const [logSearchQuery, setLogSearchQuery] = React.useState('')\n const [selectedLogLevel, setSelectedLogLevel] = React.useState('ALL')\n const [isWrapped, setIsWrapped] = React.useState(false)\n const [copiedLogs, setCopiedLogs] = React.useState(false)\n\n const filteredLogs = React.useMemo(() => {\n const query = logSearchQuery.trim().toLowerCase()\n const level = selectedLogLevel\n\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\n return matchesLevel && matchesQuery\n })\n }, [node, logSearchQuery, selectedLogLevel])\n\n const handleCopyNodeLogs = React.useCallback(async () => {\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 setCopiedLogs(true)\n setTimeout(() => {\n setCopiedLogs(false)\n }, 2000)\n } catch {\n // Clipboard fallback\n }\n }, [node])\n\n return (\n \n \n
\n {/* Selected Task Metadata */}\n
\n
\n
\n \n
\n {node.name}\n {node.categoryLabel}\n \n \n Success\n \n
\n\n {/* Task Telemetry Chips */}\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 {/* Log Controls & Filters */}\n
\n {/* Search Logs Input */}\n
\n \n setLogSearchQuery(e.target.value)}\n type=\"text\"\n placeholder=\"Filter logs...\"\n className=\"border-border bg-muted/40 text-foreground placeholder:text-muted-foreground focus-visible:border-ring focus-visible:ring-ring/50 h-8 w-full rounded-md border pr-7 pl-8 text-xs focus-visible:ring-[2px] focus-visible:outline-none\"\n />\n {logSearchQuery && (\n setLogSearchQuery('')}\n >\n \n \n )}\n
\n\n {/* Wrap Toggle */}\n \n\n {/* Copy Logs Button */}\n \n {copiedLogs ? : }\n {copiedLogs ? 'Logs Copied' : 'Copy Logs'}\n \n
\n
\n \n\n \n \n \n \n \n Stdout Logs ({node.logs.length})\n \n \n \n Task Parameters\n \n \n \n Lineage & Schema\n \n \n \n Quality Assertions ({node.qualityChecks.length})\n \n \n\n {/* Tab 1: Stdout Execution Logs */}\n \n {/* Filter Bar for Log Levels */}\n
\n
\n Level:\n {(['ALL', 'INFO', 'WARN', 'SQL', 'SUCCESS'] as const).map((lvl) => (\n setSelectedLogLevel(lvl)}\n >\n {lvl}\n \n ))}\n
\n\n
\n Showing {filteredLogs.length} of {node.logs.length} lines\n
\n
\n\n {/* Dark Terminal Window Container */}\n
\n {/* Terminal Header Bar */}\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 {/* Terminal Body Viewport */}\n
\n {filteredLogs.length === 0 ? (\n
\n No log entries matching current search or level filters.\n
\n ) : (\n filteredLogs.map((log, idx) => (\n \n {/* Line Number */}\n \n {idx + 1}\n \n\n {/* Timestamp */}\n [{log.timestamp}]\n\n {/* Level Tag */}\n \n [{log.level}]\n \n\n {/* Source Tag */}\n [{log.source}]\n\n {/* Message */}\n {log.message}\n
\n ))\n )}\n
\n \n \n\n {/* Tab 2: Task Parameters & Config */}\n \n
\n
\n Task Configuration Manifest ({node.operator})\n
\n
\n {Object.entries(node.configParams).map(([key, val]) => (\n
\n {key}\n \n {val}\n \n
\n ))}\n
\n
\n \n\n {/* Tab 3: Lineage & Schema */}\n \n
\n {/* Upstream Dependencies */}\n
\n
\n \n Upstream Dependencies ({node.upstream.length})\n
\n {node.upstream.length === 0 ? (\n
\n Root extraction node. No upstream parent dependencies.\n
\n ) : (\n
\n {node.upstream.map((up) => (\n onSelectNode(up)}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n onSelectNode(up)\n }\n }}\n >\n {up}\n \n Resolved\n \n
\n ))}\n
\n )}\n
\n\n {/* Downstream Dependents */}\n
\n
\n \n Downstream Consumers ({node.downstream.length})\n
\n {node.downstream.length === 0 ? (\n
\n Terminal node. Emits finalized reporting dataset.\n
\n ) : (\n
\n {node.downstream.map((down) => (\n onSelectNode(down)}\n onKeyDown={(e) => {\n if (e.key === 'Enter' || e.key === ' ') {\n e.preventDefault()\n onSelectNode(down)\n }\n }}\n >\n {down}\n \n Triggered\n \n
\n ))}\n
\n )}\n \n \n\n {/* Target Schema Column Table */}\n
\n
\n Target Dataset Schema ({node.target})\n
\n
\n \n \n \n \n \n \n \n \n \n \n {node.schemaColumns.map((col) => (\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 {/* Tab 4: Quality Assertions */}\n \n
\n
\n Great Expectations / Assertion Test Suite\n \n {node.qualityChecks.filter((c) => c.status === 'passed').length} / {node.qualityChecks.length} PASSED\n \n
\n
\n {node.qualityChecks.map((check) => (\n \n
\n
\n \n PASSED\n \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}\n", "type": "registry:page", "target": "~/components/blocks/EtlNodeDetail.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/progress.json", "https://uipkge.dev/r/react/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" ] }