This workflow follows the Emailsend → Postgres recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"name": "Data ETL Pipeline - Daily Sales Report",
"nodes": [
{
"parameters": {
"rule": {
"interval": [
{
"field": "cronExpression",
"expression": "0 2 * * *"
}
]
}
},
"id": "daily-trigger",
"name": "Daily at 2 AM",
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1,
"position": [
250,
300
]
},
{
"parameters": {
"functionCode": "// Calculate date range for yesterday\nconst today = new Date();\nconst yesterday = new Date(today);\nyesterday.setDate(yesterday.getDate() - 1);\n\nconst startDate = new Date(yesterday);\nstartDate.setHours(0, 0, 0, 0);\n\nconst endDate = new Date(yesterday);\nendDate.setHours(23, 59, 59, 999);\n\nreturn [{\n json: {\n reportDate: yesterday.toISOString().split('T')[0],\n startTimestamp: startDate.toISOString(),\n endTimestamp: endDate.toISOString(),\n reportId: `REPORT-${yesterday.toISOString().split('T')[0]}-${Date.now()}`\n }\n}];"
},
"id": "prepare-dates",
"name": "Prepare Date Range",
"type": "n8n-nodes-base.function",
"typeVersion": 1,
"position": [
450,
300
]
},
{
"parameters": {
"operation": "executeQuery",
"query": "SELECT \n COUNT(*) as total_orders,\n SUM(total_amount) as total_revenue,\n AVG(total_amount) as avg_order_value,\n COUNT(DISTINCT customer_id) as unique_customers\nFROM orders\nWHERE created_at >= '{{$json.startTimestamp}}'\n AND created_at <= '{{$json.endTimestamp}}'\n AND status IN ('completed', 'shipped')",
"additionalFields": {}
},
"id": "extract-orders",
"name": "Extract Order Data",
"type": "n8n-nodes-base.postgres",
"typeVersion": 1,
"position": [
650,
200
]
},
{
"parameters": {
"operation": "executeQuery",
"query": "SELECT \n p.category,\n COUNT(DISTINCT oi.order_id) as orders_count,\n SUM(oi.quantity) as units_sold,\n SUM(oi.quantity * oi.unit_price) as category_revenue\nFROM order_items oi\nJOIN products p ON oi.product_id = p.id\nJOIN orders o ON oi.order_id = o.id\nWHERE o.created_at >= '{{$node[\"Prepare Date Range\"].json.startTimestamp}}'\n AND o.created_at <= '{{$node[\"Prepare Date Range\"].json.endTimestamp}}'\n AND o.status IN ('completed', 'shipped')\nGROUP BY p.category\nORDER BY category_revenue DESC",
"additionalFields": {}
},
"id": "extract-categories",
"name": "Extract Category Sales",
"type": "n8n-nodes-base.postgres",
"typeVersion": 1,
"position": [
650,
300
]
},
{
"parameters": {
"operation": "executeQuery",
"query": "SELECT \n p.id as product_id,\n p.name as product_name,\n p.sku,\n SUM(oi.quantity) as units_sold,\n SUM(oi.quantity * oi.unit_price) as revenue,\n p.stock_quantity as current_stock\nFROM order_items oi\nJOIN products p ON oi.product_id = p.id\nJOIN orders o ON oi.order_id = o.id\nWHERE o.created_at >= '{{$node[\"Prepare Date Range\"].json.startTimestamp}}'\n AND o.created_at <= '{{$node[\"Prepare Date Range\"].json.endTimestamp}}'\n AND o.status IN ('completed', 'shipped')\nGROUP BY p.id, p.name, p.sku, p.stock_quantity\nORDER BY units_sold DESC\nLIMIT 20",
"additionalFields": {}
},
"id": "extract-products",
"name": "Extract Top Products",
"type": "n8n-nodes-base.postgres",
"typeVersion": 1,
"position": [
650,
400
]
},
{
"parameters": {
"functionCode": "// Transform and aggregate data\nconst dateInfo = $node['Prepare Date Range'].json;\nconst orderStats = $node['Extract Order Data'].json[0];\nconst categoryData = $node['Extract Category Sales'].json;\nconst topProducts = $node['Extract Top Products'].json;\n\n// Calculate additional metrics\nconst conversionRate = orderStats.unique_customers > 0 \n ? (orderStats.total_orders / orderStats.unique_customers * 100).toFixed(2)\n : 0;\n\n// Find best performing category\nconst bestCategory = categoryData.length > 0 ? categoryData[0] : null;\n\n// Calculate inventory alerts\nconst lowStockProducts = topProducts.filter(p => p.current_stock < p.units_sold * 7);\n\n// Format currency\nconst formatCurrency = (amount) => {\n return new Intl.NumberFormat('en-US', {\n style: 'currency',\n currency: 'USD'\n }).format(amount || 0);\n};\n\nreturn [{\n json: {\n reportId: dateInfo.reportId,\n reportDate: dateInfo.reportDate,\n summary: {\n totalOrders: parseInt(orderStats.total_orders),\n totalRevenue: formatCurrency(orderStats.total_revenue),\n avgOrderValue: formatCurrency(orderStats.avg_order_value),\n uniqueCustomers: parseInt(orderStats.unique_customers),\n conversionRate: `${conversionRate}%`\n },\n categoryPerformance: categoryData.map(cat => ({\n category: cat.category,\n revenue: formatCurrency(cat.category_revenue),\n unitsSold: parseInt(cat.units_sold),\n orderCount: parseInt(cat.orders_count)\n })),\n topProducts: topProducts.slice(0, 10).map(prod => ({\n name: prod.product_name,\n sku: prod.sku,\n unitsSold: parseInt(prod.units_sold),\n revenue: formatCurrency(prod.revenue),\n stockLevel: parseInt(prod.current_stock),\n needsRestock: prod.current_stock < prod.units_sold * 7\n })),\n alerts: {\n lowStockCount: lowStockProducts.length,\n lowStockProducts: lowStockProducts.map(p => ({\n name: p.product_name,\n sku: p.sku,\n currentStock: p.current_stock,\n weeklyVelocity: Math.round(p.units_sold * 7)\n }))\n },\n metadata: {\n generatedAt: new Date().toISOString(),\n dataSource: 'Production Database',\n reportType: 'daily_sales_summary'\n }\n }\n}];"
},
"id": "transform-data",
"name": "Transform & Aggregate",
"type": "n8n-nodes-base.function",
"typeVersion": 1,
"position": [
850,
300
]
},
{
"parameters": {
"operation": "upload",
"bucketName": "analytics-reports",
"fileName": "={{$json.reportDate}}/sales-report-{{$json.reportId}}.json",
"binaryPropertyName": "data",
"additionalFields": {
"storageClass": "STANDARD_IA",
"acl": "private"
}
},
"id": "store-s3",
"name": "Store in S3",
"type": "n8n-nodes-base.awsS3",
"typeVersion": 1,
"position": [
1250,
200
]
},
{
"parameters": {
"content": "={{JSON.stringify($json, null, 2)}}",
"fileName": "={{$json.reportId}}.json",
"mimeType": "application/json"
},
"id": "create-file",
"name": "Create Report File",
"type": "n8n-nodes-base.moveBinaryData",
"typeVersion": 1,
"position": [
1050,
300
]
},
{
"parameters": {
"resource": "database",
"operation": "insert",
"table": "analytics_reports",
"columns": "report_id,report_date,report_type,total_revenue,total_orders,s3_path,created_at",
"additionalFields": {}
},
"id": "log-report",
"name": "Log Report Metadata",
"type": "n8n-nodes-base.postgres",
"typeVersion": 1,
"position": [
1450,
200
]
},
{
"parameters": {
"fromEmail": "analytics@company.com",
"toEmail": "management@company.com",
"subject": "Daily Sales Report - {{$node['Transform & Aggregate'].json.reportDate}}",
"text": "Please find attached the daily sales report for {{$node['Transform & Aggregate'].json.reportDate}}.\n\nKey Highlights:\n- Total Revenue: {{$node['Transform & Aggregate'].json.summary.totalRevenue}}\n- Total Orders: {{$node['Transform & Aggregate'].json.summary.totalOrders}}\n- Average Order Value: {{$node['Transform & Aggregate'].json.summary.avgOrderValue}}\n- Unique Customers: {{$node['Transform & Aggregate'].json.summary.uniqueCustomers}}\n\n{{$node['Transform & Aggregate'].json.alerts.lowStockCount}} products need restocking.",
"attachments": {
"binaryProperties": [
"data"
]
},
"options": {}
},
"id": "email-report",
"name": "Email Report",
"type": "n8n-nodes-base.emailSend",
"typeVersion": 1,
"position": [
1450,
300
]
},
{
"parameters": {
"conditions": {
"number": [
{
"value1": "={{$node['Transform & Aggregate'].json.alerts.lowStockCount}}",
"operation": "larger",
"value2": 0
}
]
}
},
"id": "check-alerts",
"name": "Has Inventory Alerts?",
"type": "n8n-nodes-base.if",
"typeVersion": 1,
"position": [
1250,
400
]
},
{
"parameters": {
"channel": "#inventory-alerts",
"text": ":warning: Low Stock Alert\n\n{{$node['Transform & Aggregate'].json.alerts.lowStockCount}} products are running low:\n\n{{$node['Transform & Aggregate'].json.alerts.lowStockProducts.map(p => `\u2022 ${p.name} (${p.sku}): ${p.currentStock} units remaining`).join('\\n')}}",
"otherOptions": {
"mrkdwn": true
}
},
"id": "slack-alert",
"name": "Send Inventory Alert",
"type": "n8n-nodes-base.slack",
"typeVersion": 1,
"position": [
1450,
400
]
},
{
"parameters": {
"values": {
"string": [
{
"name": "status",
"value": "completed"
}
],
"number": [
{
"name": "recordsProcessed",
"value": "={{$node['Extract Order Data'].json[0].total_orders + $node['Extract Category Sales'].json.length + $node['Extract Top Products'].json.length}}"
}
]
},
"options": {}
},
"id": "completion-status",
"name": "Set Completion Status",
"type": "n8n-nodes-base.set",
"typeVersion": 1,
"position": [
1650,
300
]
}
],
"connections": {
"Daily at 2 AM": {
"main": [
[
{
"node": "Prepare Date Range",
"type": "main",
"index": 0
}
]
]
},
"Prepare Date Range": {
"main": [
[
{
"node": "Extract Order Data",
"type": "main",
"index": 0
},
{
"node": "Extract Category Sales",
"type": "main",
"index": 0
},
{
"node": "Extract Top Products",
"type": "main",
"index": 0
}
]
]
},
"Extract Order Data": {
"main": [
[
{
"node": "Transform & Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Extract Category Sales": {
"main": [
[
{
"node": "Transform & Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Extract Top Products": {
"main": [
[
{
"node": "Transform & Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Transform & Aggregate": {
"main": [
[
{
"node": "Create Report File",
"type": "main",
"index": 0
},
{
"node": "Has Inventory Alerts?",
"type": "main",
"index": 0
}
]
]
},
"Create Report File": {
"main": [
[
{
"node": "Store in S3",
"type": "main",
"index": 0
},
{
"node": "Email Report",
"type": "main",
"index": 0
}
]
]
},
"Store in S3": {
"main": [
[
{
"node": "Log Report Metadata",
"type": "main",
"index": 0
}
]
]
},
"Log Report Metadata": {
"main": [
[
{
"node": "Set Completion Status",
"type": "main",
"index": 0
}
]
]
},
"Email Report": {
"main": [
[
{
"node": "Set Completion Status",
"type": "main",
"index": 0
}
]
]
},
"Has Inventory Alerts?": {
"main": [
[
{
"node": "Send Inventory Alert",
"type": "main",
"index": 0
}
],
[]
]
},
"Send Inventory Alert": {
"main": [
[
{
"node": "Set Completion Status",
"type": "main",
"index": 0
}
]
]
}
}
}
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About this workflow
Data ETL Pipeline - Daily Sales Report. Uses postgres, awsS3, moveBinaryData, emailSend. Scheduled trigger; 13 nodes.
Source: https://github.com/pvdyck/n8n-test-framework/blob/dabf65c294eba226a14b439ef672584f48315473/examples/workflows/data-etl-pipeline.json — original creator credit. Request a take-down →
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