{ "$schema": "https://ui.shadcn.com/schema/registry-item.json", "name": "lakehouse-table-optimizer", "title": "Lakehouse Table Optimizer", "type": "registry:page", "files": [ { "path": "packages/registry-react/blocks/lakehouse-table-optimizer/LakehouseTableOptimizer.tsx", "content": "'use client'\n\nimport * as React from 'react'\nimport {\n ArrowDownRight,\n Check,\n CheckCircle2,\n Clock,\n Copy,\n Database,\n FileCode2,\n FileText,\n FolderArchive,\n HardDrive,\n History,\n Layers,\n RefreshCw,\n Search,\n Server,\n Terminal,\n Trash2,\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 { 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 type SnapshotOperation = 'MERGE' | 'APPEND' | 'OPTIMIZE' | 'REPLACE_PARTITION'\nexport type SqlSyntaxMode = 'system_time' | 'system_version' | 'pyspark' | 'trino'\n\nexport interface TableSnapshot {\n id: string\n numericId: string\n timestamp: string\n relativeTime: string\n operation: SnapshotOperation\n rowsAdded: string\n rowsDeleted: string\n author: string\n summary: string\n manifestFile: string\n filesAdded: number\n filesRemoved: number\n byteDelta: string\n}\n\nexport interface PartitionRecord {\n partitionKey: string\n fileCount: number\n avgFileSize: string\n totalSize: string\n skippedPercentage: number\n zOrderScore: string\n status: 'Optimized' | 'Compacting' | 'Needs Z-Order'\n lastClustered: string\n}\n\nexport interface TableDetails {\n name: string\n format: string\n totalSize: string\n rowCount: string\n statusText: string\n catalog: string\n storageLocation: string\n lastCompacted: string\n compressionCodec: string\n}\n\nexport interface LakehouseTableOptimizerProps {\n initialTable?: TableDetails\n initialSnapshots?: TableSnapshot[]\n initialPartitions?: PartitionRecord[]\n className?: string\n}\n\nconst defaultTable: TableDetails = {\n name: 'lakehouse.silver_customer_events',\n format: 'Apache Iceberg v2 · Parquet',\n totalSize: '4.2 TB',\n rowCount: '1.48B Rows',\n statusText: 'Table Optimized · 0 Small Files',\n catalog: 'AWS Glue Data Catalog (us-east-1)',\n storageLocation: 's3://lakehouse-data-prod/silver/customer_events/',\n lastCompacted: '28 mins ago',\n compressionCodec: 'ZSTD (Level 7)',\n}\n\nconst defaultSnapshots: TableSnapshot[] = [\n {\n id: 'snap_84920194821',\n numericId: '84920194821',\n timestamp: '2026-08-21 09:30:00 UTC',\n relativeTime: '12 mins ago',\n operation: 'MERGE',\n rowsAdded: '+142,500',\n rowsDeleted: '-18,200',\n author: 'spark-streaming-pipeline',\n summary: \"Upserted late-arriving customer clickstream events from Kafka topic 'customer.events.prod'\",\n manifestFile: 'snap-84920194821-1-m0.avro',\n filesAdded: 4,\n filesRemoved: 3,\n byteDelta: '+128 MB',\n },\n {\n id: 'snap_84920183104',\n numericId: '84920183104',\n timestamp: '2026-08-21 08:00:00 UTC',\n relativeTime: '1 hour ago',\n operation: 'OPTIMIZE',\n rowsAdded: '+0',\n rowsDeleted: '-0',\n author: 'iceberg-compactor-daemon',\n summary: 'Bin-pack compaction rewrote 4,210 small files into 42 optimal 128MB Parquet data files',\n manifestFile: 'snap-84920183104-1-m0.avro',\n filesAdded: 42,\n filesRemoved: 4210,\n byteDelta: '-4.1 GB (compaction gain)',\n },\n {\n id: 'snap_84920152918',\n numericId: '84920152918',\n timestamp: '2026-08-21 06:15:00 UTC',\n relativeTime: '3 hours ago',\n operation: 'APPEND',\n rowsAdded: '+890,200',\n rowsDeleted: '-0',\n author: 'batch-ingest-hourly',\n summary: 'Micro-batch append for EMEA checkout and cart telemetry session events',\n manifestFile: 'snap-84920152918-1-m0.avro',\n filesAdded: 18,\n filesRemoved: 0,\n byteDelta: '+2.2 GB',\n },\n {\n id: 'snap_84920120195',\n numericId: '84920120195',\n timestamp: '2026-08-20 23:45:00 UTC',\n relativeTime: '9 hours ago',\n operation: 'REPLACE_PARTITION',\n rowsAdded: '+3,410,000',\n rowsDeleted: '-3,398,000',\n author: 'dbt-backfill-runner',\n summary: \"Replaced partition 'event_date=2026-08-20' following upstream IP geolocation schema enrichment\",\n manifestFile: 'snap-84920120195-1-m0.avro',\n filesAdded: 28,\n filesRemoved: 28,\n byteDelta: '+140 MB',\n },\n {\n id: 'snap_84919984120',\n numericId: '84919984120',\n timestamp: '2026-08-20 18:30:00 UTC',\n relativeTime: '15 hours ago',\n operation: 'APPEND',\n rowsAdded: '+1,205,400',\n rowsDeleted: '-0',\n author: 'batch-ingest-hourly',\n summary: 'Hourly ingestion run for US-East customer checkout telemetry records',\n manifestFile: 'snap-84919984120-1-m0.avro',\n filesAdded: 24,\n filesRemoved: 0,\n byteDelta: '+3.1 GB',\n },\n]\n\nconst defaultPartitions: PartitionRecord[] = [\n {\n partitionKey: 'event_date=2026-08-21',\n fileCount: 42,\n avgFileSize: '128.2 MB',\n totalSize: '5.38 GB',\n skippedPercentage: 98.4,\n zOrderScore: '98.4%',\n status: 'Optimized',\n lastClustered: '28m ago',\n },\n {\n partitionKey: 'event_date=2026-08-20',\n fileCount: 40,\n avgFileSize: '127.9 MB',\n totalSize: '5.12 GB',\n skippedPercentage: 97.8,\n zOrderScore: '97.8%',\n status: 'Optimized',\n lastClustered: '9h ago',\n },\n {\n partitionKey: 'event_date=2026-08-19',\n fileCount: 38,\n avgFileSize: '128.0 MB',\n totalSize: '4.86 GB',\n skippedPercentage: 96.9,\n zOrderScore: '96.9%',\n status: 'Optimized',\n lastClustered: '1d ago',\n },\n {\n partitionKey: 'event_date=2026-08-18',\n fileCount: 44,\n avgFileSize: '126.8 MB',\n totalSize: '5.58 GB',\n skippedPercentage: 95.8,\n zOrderScore: '95.8%',\n status: 'Optimized',\n lastClustered: '2d ago',\n },\n {\n partitionKey: 'event_date=2026-08-17',\n fileCount: 36,\n avgFileSize: '128.5 MB',\n totalSize: '4.62 GB',\n skippedPercentage: 97.1,\n zOrderScore: '97.1%',\n status: 'Optimized',\n lastClustered: '3d ago',\n },\n {\n partitionKey: 'event_date=2026-08-16',\n fileCount: 39,\n avgFileSize: '127.4 MB',\n totalSize: '4.97 GB',\n skippedPercentage: 96.5,\n zOrderScore: '96.5%',\n status: 'Optimized',\n lastClustered: '4d ago',\n },\n]\n\nfunction getOperationBadgeVariant(\n op: SnapshotOperation,\n): 'default' | 'secondary' | 'outline' | 'success' | 'warning' | 'info' {\n switch (op) {\n case 'OPTIMIZE':\n return 'success'\n case 'MERGE':\n return 'info'\n case 'APPEND':\n return 'secondary'\n case 'REPLACE_PARTITION':\n return 'warning'\n default:\n return 'outline'\n }\n}\n\nexport function LakehouseTableOptimizer({\n initialTable = defaultTable,\n initialSnapshots = defaultSnapshots,\n initialPartitions = defaultPartitions,\n className,\n}: LakehouseTableOptimizerProps) {\n const [table] = React.useState(initialTable)\n const [snapshots] = React.useState(initialSnapshots)\n const [partitions] = React.useState(initialPartitions)\n\n const [selectedSnapshotId, setSelectedSnapshotId] = React.useState('snap_84920194821')\n const [sqlSyntaxMode, setSqlSyntaxMode] = React.useState('system_time')\n const [snapshotFilter, setSnapshotFilter] = React.useState('ALL')\n const [snapshotSearch, setSnapshotSearch] = React.useState('')\n const [partitionSearch, setPartitionSearch] = React.useState('')\n const [activeTab, setActiveTab] = React.useState('snapshots')\n\n const [isCompacting, setIsCompacting] = React.useState(false)\n const [isExpiring, setIsExpiring] = React.useState(false)\n const [actionNotification, setActionNotification] = React.useState(null)\n const [copiedQuery, setCopiedQuery] = React.useState(false)\n\n const selectedSnapshot = React.useMemo(() => {\n return snapshots.find((s) => s.id === selectedSnapshotId) ?? snapshots[0]\n }, [snapshots, selectedSnapshotId])\n\n const filteredSnapshots = React.useMemo(() => {\n return snapshots.filter((snap) => {\n const matchesOp = snapshotFilter === 'ALL' || snap.operation === snapshotFilter\n const query = snapshotSearch.toLowerCase().trim()\n const matchesSearch =\n query === '' ||\n snap.id.toLowerCase().includes(query) ||\n snap.author.toLowerCase().includes(query) ||\n snap.summary.toLowerCase().includes(query) ||\n snap.operation.toLowerCase().includes(query)\n return matchesOp && matchesSearch\n })\n }, [snapshots, snapshotFilter, snapshotSearch])\n\n const filteredPartitions = React.useMemo(() => {\n const query = partitionSearch.toLowerCase().trim()\n if (!query) return partitions\n return partitions.filter((p) => p.partitionKey.toLowerCase().includes(query))\n }, [partitions, partitionSearch])\n\n const generatedQuery = React.useMemo(() => {\n const currentSnap = selectedSnapshot\n const tableName = table.name\n\n switch (sqlSyntaxMode) {\n case 'system_time':\n return `SELECT * FROM ${tableName}\\nFOR SYSTEM_TIME AS OF '${currentSnap.timestamp}';`\n case 'system_version':\n return `SELECT * FROM ${tableName}\\nFOR SYSTEM_VERSION AS OF ${currentSnap.numericId};`\n case 'pyspark':\n return `# PySpark Iceberg time-travel query\\ndf = spark.read \\\\\\n .format(\"iceberg\") \\\\\\n .option(\"as-of-timestamp\", \"${currentSnap.timestamp}\") \\\\\\n .load(\"${tableName}\")\\n\\ndf.filter(\"customer_id = 'cust_98241'\") \\\\\\n .select(\"event_id\", \"event_type\", \"event_date\", \"payload\") \\\\\\n .show(10)`\n case 'trino':\n return `SELECT event_id, customer_id, event_type, event_date\\nFROM ${tableName}\\nFOR VERSION AS OF ${currentSnap.numericId}\\nWHERE event_type = 'CHECKOUT_COMPLETED'\\nLIMIT 50;`\n default:\n return `SELECT * FROM ${tableName}\\nFOR SYSTEM_TIME AS OF '${currentSnap.timestamp}';`\n }\n }, [selectedSnapshot, table.name, sqlSyntaxMode])\n\n const handleCopyQuery = () => {\n if (typeof navigator !== 'undefined' && navigator.clipboard) {\n navigator.clipboard.writeText(generatedQuery)\n setCopiedQuery(true)\n setTimeout(() => {\n setCopiedQuery(false)\n }, 2000)\n }\n }\n\n const runCompaction = () => {\n if (isCompacting) return\n setIsCompacting(true)\n setActionNotification(null)\n\n setTimeout(() => {\n setIsCompacting(false)\n setActionNotification(\n 'Compaction & Vacuum complete: 14,280 small files compacted into 142 optimal 128MB Parquet files. Reclaimed 1.2 TB storage.',\n )\n setTimeout(() => {\n setActionNotification(null)\n }, 6000)\n }, 1800)\n }\n\n const expireSnapshots = () => {\n if (isExpiring) return\n setIsExpiring(true)\n setActionNotification(null)\n\n setTimeout(() => {\n setIsExpiring(false)\n setActionNotification(\n 'Snapshot expiration finished: Removed 12 stale snapshots older than 30-day retention window. Purged 872 GB metadata & unreferenced parquet files.',\n )\n setTimeout(() => {\n setActionNotification(null)\n }, 6000)\n }, 1400)\n }\n\n return (\n
\n {/* Header Section */}\n
\n
\n {/* Table Identity */}\n
\n
\n \n
\n
\n
\n

\n {table.name}\n

\n \n \n {table.statusText}\n \n
\n
\n \n \n {table.format}\n \n \n \n {table.totalSize} · {table.rowCount}\n \n \n \n {table.catalog}\n \n
\n
\n
\n\n {/* Header Actions */}\n
\n \n {!isExpiring ? (\n \n ) : (\n \n )}\n {isExpiring ? 'Expiring Stale...' : 'Expire Snapshots'}\n \n\n \n {!isCompacting ? : }\n {isCompacting ? 'Compacting Table...' : 'Run Compaction & Vacuum'}\n \n
\n
\n\n {/* Action Toast / Banner */}\n {actionNotification && (\n
\n
\n \n {actionNotification}\n
\n setActionNotification(null)}\n >\n Dismiss\n ×\n \n
\n )}\n
\n\n {/* 4 Table Health & Storage Telemetry Cards */}\n
\n {/* Card 1: Total Partitions */}\n \n \n \n Total Partitions\n \n
\n \n
\n \n \n
\n
1,420
\n \n 100% Healthy\n \n
\n

1,420 Partitions · Date Partitioned

\n
\n \n
\n event_date (YYYY-MM-DD)\n 0 unpartitioned\n
\n
\n \n \n\n {/* Card 2: Small Files Neutralized */}\n \n \n \n Small Files Neutralized\n \n
\n \n
\n \n \n
\n
14,280
\n \n 99.0% Reduction\n \n
\n

14,280 small files compacted -> 142 optimal 128MB files

\n
\n \n
\n Target: 128 MB\n 0 files <32MB\n
\n
\n \n \n\n {/* Card 3: Snapshot History Count */}\n \n \n \n Snapshot History\n \n
\n \n
\n \n \n
\n
48
\n \n 30-Day Window\n \n
\n

48 Active Snapshots · 30-Day Time Travel

\n
\n \n
\n Oldest: Jul 22, 2026\n 4.8 MB metadata\n
\n
\n \n \n\n {/* Card 4: Storage Optimization Savings */}\n \n \n \n Storage Optimization\n \n
\n \n
\n \n \n
\n
1.2 TB
\n \n $240/mo Saved\n \n
\n

1.2 TB freed via Vacuum · $240/mo saved

\n
\n \n
\n 872 GB stale · 328 GB orphan\n -28.5% S3 Cost\n
\n
\n \n \n
\n\n {/* Main Lakehouse Inspection & Control Tabs */}\n \n \n \n \n Snapshots & Time-Travel\n \n \n \n Partitions & Z-Ordering\n \n \n \n Compaction & Vacuum\n \n \n \n File Sizing & Distribution\n \n \n\n {/* TAB 1: SNAPSHOTS & TIME-TRAVEL QUERY PICKER */}\n \n
\n {/* Left: Interactive Timeline (7 Cols) */}\n
\n \n \n
\n
\n Snapshot Commit History\n \n Select any snapshot to generate reproducible time-travel queries\n \n
\n {/* Filter Pill Buttons */}\n
\n {(['ALL', 'MERGE', 'OPTIMIZE', 'APPEND', 'REPLACE_PARTITION'] as const).map((op) => (\n setSnapshotFilter(op)}\n >\n {op}\n \n ))}\n
\n
\n\n {/* Search Filter */}\n
\n \n setSnapshotSearch(e.target.value)}\n type=\"text\"\n placeholder=\"Search snapshot ID, author, summary...\"\n className=\"border-border bg-background text-foreground placeholder:text-muted-foreground focus-visible:ring-ring w-full rounded-md border py-1.5 pr-3 pl-8 text-xs focus-visible:ring-2 focus-visible:outline-none\"\n />\n
\n \n\n \n\n \n
\n {filteredSnapshots.map((snap) => (\n setSelectedSnapshotId(snap.id)}\n >\n {/* Timeline Dot Indicator */}\n
\n {snap.operation === 'MERGE' && }\n {snap.operation === 'OPTIMIZE' && }\n {snap.operation === 'APPEND' && }\n {snap.operation === 'REPLACE_PARTITION' && }\n
\n\n {/* Snapshot Details */}\n
\n
\n
\n {snap.id}\n \n {snap.operation}\n \n {selectedSnapshotId === snap.id && (\n \n Active Time-Travel Target\n \n )}\n
\n \n {snap.relativeTime}\n \n
\n\n

{snap.summary}

\n\n {/* Metric Badges Row */}\n
\n
\n {snap.rowsAdded}\n /\n {snap.rowsDeleted} rows\n
\n
\n \n {snap.byteDelta}\n
\n
\n Author:\n {snap.author}\n
\n
\n
\n
\n ))}\n
\n \n \n
\n\n {/* Right: Time-Travel Query Generator & Helper (5 Cols) */}\n
\n \n \n
\n
\n Time-Travel Query Picker\n \n Instant query generator for snapshot audit & reconciliation\n \n
\n \n {selectedSnapshot.numericId}\n \n
\n\n {/* Syntax Mode Buttons */}\n
\n setSqlSyntaxMode('system_time')}\n >\n SYSTEM_TIME\n \n setSqlSyntaxMode('system_version')}\n >\n VERSION\n \n setSqlSyntaxMode('pyspark')}\n >\n PySpark\n \n setSqlSyntaxMode('trino')}\n >\n Trino SQL\n \n
\n \n\n \n\n \n {/* Target Snapshot Meta Summary */}\n
\n
\n Target Snapshot:\n {selectedSnapshot.id}\n
\n
\n Point-in-Time:\n {selectedSnapshot.timestamp}\n
\n
\n Commit Operation:\n \n {selectedSnapshot.operation}\n \n
\n
\n\n {/* Code Block with Copy */}\n
\n
\n \n \n Query table as of snapshot:\n \n \n {copiedQuery ? (\n \n ) : (\n \n )}\n {copiedQuery ? 'Copied' : 'Copy'}\n \n
\n 
\n                      {generatedQuery}\n
\n
\n\n {/* Telemetry Stats about Query */}\n
\n
\n Estimated Scan Overhead:\n 42ms · 96.4% skipped\n
\n
\n Manifest Avro File:\n {selectedSnapshot.manifestFile}\n
\n
\n Storage Engine:\n Apache Iceberg Native Parquet Reader\n
\n
\n \n \n
\n
\n \n\n {/* TAB 2: PARTITIONS & Z-ORDER CLUSTERING */}\n \n {/* Table Partition Maintenance & Z-Order Clustering Status Card */}\n \n \n
\n
\n \n \n Partition Health & Multidimensional Z-Order Clustering\n \n \n Z-Order clustered by customer_id,{' '}\n event_type to maximize Parquet\n min/max statistics pruning.\n \n
\n\n {/* Search Partitions */}\n
\n \n setPartitionSearch(e.target.value)}\n type=\"text\"\n placeholder=\"Filter partitions...\"\n className=\"border-border bg-background text-foreground placeholder:text-muted-foreground focus-visible:ring-ring w-full rounded-md border py-1.5 pr-3 pl-8 text-xs focus-visible:ring-2 focus-visible:outline-none\"\n />\n
\n
\n\n {/* Clustering Telemetry Overview Grid */}\n
\n
\n Clustered Columns:\n
customer_id, event_type
\n
\n
\n Data Skipping Efficiency:\n
96.4% Row-Groups Skipped
\n
\n
\n Clustering Curve Algorithm:\n
Hilbert Space-Filling (Depth 16)
\n
\n
\n Compression Codec:\n
ZSTD Level 7 · Parquet v2
\n
\n
\n \n\n \n\n \n
\n \n \n \n Partition Key\n File Count\n Avg File Size\n Total Data Size\n Data Skipping\n Z-Order Status\n Action\n \n \n \n {filteredPartitions.map((partition) => (\n \n \n {partition.partitionKey}\n \n {partition.fileCount} files\n {partition.avgFileSize}\n {partition.totalSize}\n \n
\n
\n {partition.skippedPercentage}%\n
\n \n
\n \n \n \n \n {partition.status}\n \n \n \n \n \n \n ))}\n \n
\n
\n \n \n \n\n {/* TAB 3: COMPACTION & VACUUM MAINTENANCE RULES */}\n \n
\n {/* Bin-Pack Compaction Rule */}\n \n \n
\n \n \n Bin-Pack Data File Compaction\n \n \n Enabled · Auto\n \n
\n \n Merges small files into optimized 128MB Parquet chunks to prevent small file bottleneck\n \n \n \n
\n
\n Target File Size:\n 128 MB\n
\n
\n Small File Threshold:\n < 32 MB\n
\n
\n Execution Schedule:\n Every 2 Hours (cron: 0 */2 * * *)\n
\n
\n Engine:\n Apache Spark Iceberg RewriteDataFiles Action\n
\n
\n \n \n\n {/* Vacuum & Orphan File Removal */}\n \n \n
\n \n \n Vacuum & Orphan File Purger\n \n \n Enabled · Daily\n \n
\n \n Hard-deletes unreferenced data files and uncommitted multi-part upload orphans in S3\n \n \n \n
\n
\n Retention Grace Period:\n 7 Days (168 Hours)\n
\n
\n Storage Reclaimed (30d):\n 1.2 TB freed\n
\n
\n Execution Schedule:\n Daily at 02:00 UTC\n
\n
\n Safety Guard:\n Explicit dry-run verification check\n
\n
\n \n \n\n {/* Snapshot Expiration Policy */}\n \n \n
\n \n \n Snapshot Expiration Policy\n \n \n 30-Day Window\n \n
\n \n Controls metadata table history retention for time-travel queries and rollback safety\n \n \n \n
\n
\n Retention Window:\n 30 Days\n
\n
\n Min Snapshots Retained:\n 20 Snapshots\n
\n
\n Clean Expired Metadata:\n Yes (Purge orphan Avro manifests)\n
\n
\n \n \n\n {/* Manifest Consolidation */}\n \n \n
\n \n \n Manifest File Consolidation\n \n \n Optimized\n \n
\n \n Rewrites small manifest files to accelerate query planning and metadata scanning\n \n \n \n
\n
\n Active Manifest Count:\n 6 Manifests\n
\n
\n Rewrite Trigger Threshold:\n > 50 Small Manifests\n
\n
\n Query Planning Overhead:\n < 15ms\n
\n
\n \n \n
\n \n\n {/* TAB 4: FILE SIZING & I/O DISTRIBUTION */}\n \n \n \n \n File Size Distribution & I/O Amplification Analysis\n \n \n Comparison of storage layout before vs. after Iceberg automatic compaction\n \n \n \n \n
\n {/* Before Compaction */}\n
\n
\n \n Before Compaction (Small File Problem)\n \n \n 18.4x I/O Amplification\n \n
\n
\n
\n Total Small Files (<4MB):\n 14,280 Files\n
\n
\n Average File Size:\n 2.4 MB\n
\n
\n Query Scan Latency (P95):\n 8.4 seconds\n
\n
\n S3 GET Request Cost:\n $380 / month\n
\n
\n
\n\n {/* After Compaction */}\n
\n
\n After Compaction (Optimal Bin-Pack)\n \n 1.02x Near-Ideal\n \n
\n
\n
\n Total Target Files (128MB):\n 142 Files\n
\n
\n Average File Size:\n 128.2 MB\n
\n
\n Query Scan Latency (P95):\n 220 milliseconds (38x speedup)\n
\n
\n S3 GET Request Cost:\n $12 / month\n
\n
\n
\n
\n \n \n \n \n \n )\n}\n\nexport default LakehouseTableOptimizer\n", "type": "registry:page", "target": "~/components/blocks/LakehouseTableOptimizer.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/separator.json", "https://uipkge.dev/r/react/table.json", "https://uipkge.dev/r/react/tabs.json" ], "description": "Apache Iceberg and Delta Lake table maintenance manager, compaction optimizer, snapshot history visualizer, and time-travel SQL query helper.", "categories": [ "devops", "app", "analytics" ] }