{"id":1791,"date":"2026-07-19T13:00:00","date_gmt":"2026-07-19T18:00:00","guid":{"rendered":"https:\/\/tolinku.com\/blog\/?p=1791"},"modified":"2026-03-07T03:37:21","modified_gmt":"2026-03-07T08:37:21","slug":"analytics-data-export","status":"publish","type":"post","link":"https:\/\/tolinku.com\/blog\/analytics-data-export\/","title":{"rendered":"Exporting Deep Link Analytics Data"},"content":{"rendered":"\n

Built-in dashboards show you what happened. Exported data lets you figure out why. When you need to join deep link clicks with revenue data, run custom cohort models, or feed analytics into a BI tool, you need the raw data outside the platform.<\/p>\n\n\n\n

This guide covers how to export deep link analytics data. For API-based integration, see analytics API integration for deep links<\/a>. For understanding which metrics matter, see deep link analytics: measuring what matters<\/a>.<\/p>\n\n\n\n

\"Tolinku\nThe analytics dashboard with date range selector, filters, charts, and breakdowns.<\/em><\/p>\n\n\n\n

What Data to Export<\/h2>\n\n\n\n

Click-Level Data<\/h3>\n\n\n\n

Each deep link click generates a record with these fields:<\/p>\n\n\n\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Field<\/th>\nDescription<\/th>\nExample<\/th>\n<\/tr>\n<\/thead>\n
click_id<\/code><\/td>\nUnique identifier<\/td>\nclk_a1b2c3d4<\/code><\/td>\n<\/tr>\n
timestamp<\/code><\/td>\nWhen the click occurred<\/td>\n2026-07-19T14:32:00Z<\/code><\/td>\n<\/tr>\n
route<\/code><\/td>\nThe deep link route<\/td>\n\/products\/summer-sale<\/code><\/td>\n<\/tr>\n
source<\/code><\/td>\nTraffic source<\/td>\nemail<\/code><\/td>\n<\/tr>\n
medium<\/code><\/td>\nMarketing medium<\/td>\nnewsletter<\/code><\/td>\n<\/tr>\n
campaign<\/code><\/td>\nCampaign name<\/td>\njuly-promo<\/code><\/td>\n<\/tr>\n
platform<\/code><\/td>\nUser's platform<\/td>\nios<\/code><\/td>\n<\/tr>\n
os_version<\/code><\/td>\nOperating system version<\/td>\n18.2<\/code><\/td>\n<\/tr>\n
browser<\/code><\/td>\nBrowser or app<\/td>\nSafari<\/code><\/td>\n<\/tr>\n
country<\/code><\/td>\nCountry from IP geolocation<\/td>\nUS<\/code><\/td>\n<\/tr>\n
city<\/code><\/td>\nCity from IP geolocation<\/td>\nSan Francisco<\/code><\/td>\n<\/tr>\n
outcome<\/code><\/td>\nWhat happened<\/td>\napp_opened<\/code><\/td>\n<\/tr>\n
latency_ms<\/code><\/td>\nTime to resolve<\/td>\n280<\/code><\/td>\n<\/tr>\n
referrer<\/code><\/td>\nHTTP referrer<\/td>\nhttps:\/\/mail.google.com<\/code><\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n

Aggregated Data<\/h3>\n\n\n\n

For large-scale analysis, aggregated exports reduce file size:<\/p>\n\n\n\n

\n\n\n\n\n\n\n
Aggregation<\/th>\nUse Case<\/th>\n<\/tr>\n<\/thead>\n
By campaign + day<\/td>\nCampaign performance trends<\/td>\n<\/tr>\n
By route + source<\/td>\nWhich sources drive which content<\/td>\n<\/tr>\n
By platform + country<\/td>\nGeographic and device segmentation<\/td>\n<\/tr>\n
By hour of day<\/td>\nTiming optimization<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n

CSV Export<\/h2>\n\n\n\n

Manual Export<\/h3>\n\n\n\n

Most analytics platforms support CSV export from the dashboard. The export typically includes all visible columns plus any applied filters.<\/p>\n\n\n\n

click_id,timestamp,route,source,medium,campaign,platform,country,outcome,latency_ms\nclk_a1b2c3d4,2026-07-19T14:32:00Z,\/products\/summer-sale,email,newsletter,july-promo,ios,US,app_opened,280\nclk_e5f6g7h8,2026-07-19T14:33:12Z,\/offers\/clearance,push,,retention-week2,android,UK,app_opened,350\nclk_i9j0k1l2,2026-07-19T14:35:45Z,\/products\/summer-sale,social,organic,,ios,DE,fallback,420\n<\/code><\/pre>\n\n\n\n
Scheduled CSV Export<\/h3>\n\n\n\n
Automate exports on a schedule:<\/p>\n\n\n\n
interface ExportConfig {\n  schedule: 'daily' | 'weekly' | 'monthly';\n  format: 'csv' | 'json' | 'parquet';\n  destination: 'email' | 's3' | 'gcs' | 'sftp';\n  filters: {\n    campaigns?: string[];\n    routes?: string[];\n    platforms?: string[];\n    dateRange?: { start: string; end: string };\n  };\n  columns: string[];\n}\n\nconst dailyExport: ExportConfig = {\n  schedule: 'daily',\n  format: 'csv',\n  destination: 's3',\n  filters: {\n    dateRange: { start: 'yesterday', end: 'yesterday' }\n  },\n  columns: [\n    'click_id', 'timestamp', 'route', 'source',\n    'medium', 'campaign', 'platform', 'country',\n    'outcome', 'latency_ms'\n  ]\n};\n<\/code><\/pre>\n\n\n\n
CSV Pitfalls<\/h3>\n\n\n\n
    \n
  1. Large files.<\/strong> A million clicks per day produces CSV files over 100MB. Use date-range filters or switch to Parquet format for large datasets.<\/li>\n
  2. Encoding.<\/strong> Ensure UTF-8 encoding, especially if campaign names or city names contain non-ASCII characters.<\/li>\n
  3. Escaping.<\/strong> URLs in fields may contain commas. Use proper CSV quoting (RFC 4180).<\/li>\n
  4. Timezone.<\/strong> Export timestamps in UTC. Convert to local time in your analysis tool, not in the export.<\/li>\n<\/ol>\n\n\n\n
    API Export<\/h2>\n\n\n\n
    REST API Queries<\/h3>\n\n\n\n
    Query analytics data programmatically for integration with custom tools:<\/p>\n\n\n\n
    async function fetchAnalytics(\n  dateRange: { start: string; end: string },\n  filters: Record<string, string>\n): Promise<ClickData[]> {\n  const params = new URLSearchParams({\n    start: dateRange.start,\n    end: dateRange.end,\n    ...filters\n  });\n\n  const response = await fetch(\n    `https:\/\/api.example.com\/v1\/analytics\/clicks?${params}`,\n    {\n      headers: {\n        'Authorization': `Bearer ${API_KEY}`,\n        'Accept': 'application\/json'\n      }\n    }\n  );\n\n  return response.json();\n}\n<\/code><\/pre>\n\n\n\n
    Pagination<\/h3>\n\n\n\n
    Analytics APIs return paginated results. Handle pagination to get complete datasets:<\/p>\n\n\n\n
    async function fetchAllPages(\n  endpoint: string,\n  params: Record<string, string>\n): Promise<any[]> {\n  const allResults: any[] = [];\n  let cursor: string | null = null;\n\n  do {\n    const queryParams = new URLSearchParams({\n      ...params,\n      limit: '1000',\n      ...(cursor ? { cursor } : {})\n    });\n\n    const response = await fetch(`${endpoint}?${queryParams}`, {\n      headers: { 'Authorization': `Bearer ${API_KEY}` }\n    });\n\n    const data = await response.json();\n    allResults.push(...data.results);\n    cursor = data.next_cursor;\n  } while (cursor);\n\n  return allResults;\n}\n<\/code><\/pre>\n\n\n\n
    Rate Limiting<\/h3>\n\n\n\n
    Analytics APIs enforce rate limits to protect the service. Plan your exports accordingly:<\/p>\n\n\n\n
    \n\n\n\n\n\n
    Approach<\/th>\nRate Limit Impact<\/th>\nBest For<\/th>\n<\/tr>\n<\/thead>\n
    Real-time queries<\/td>\nHigh (many small requests)<\/td>\nDashboards, alerts<\/td>\n<\/tr>\n
    Batch export (daily)<\/td>\nLow (one large request)<\/td>\nReporting, data warehouse<\/td>\n<\/tr>\n
    Streaming (webhooks)<\/td>\nNone (push-based)<\/td>\nReal-time processing<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Webhook Export<\/h2>\n\n\n\n
    Event-Driven Export<\/h3>\n\n\n\n
    Instead of pulling data, have the analytics platform push events to your endpoint:<\/p>\n\n\n\n
    \/\/ Webhook receiver\napp.post('\/webhooks\/deep-link-clicks', (req, res) => {\n  const event = req.body;\n\n  \/\/ Validate webhook signature\n  const signature = req.headers['x-webhook-signature'];\n  if (!verifySignature(event, signature, WEBHOOK_SECRET)) {\n    return res.status(401).json({ error: 'Invalid signature' });\n  }\n\n  \/\/ Process the event\n  switch (event.type) {\n    case 'click':\n      processClick(event.data);\n      break;\n    case 'conversion':\n      processConversion(event.data);\n      break;\n    case 'install':\n      processInstall(event.data);\n      break;\n  }\n\n  res.status(200).json({ received: true });\n});\n<\/code><\/pre>\n\n\n\n
    Webhook vs Batch Export<\/h3>\n\n\n\n
    \n\n\n\n\n\n\n\n
    Factor<\/th>\nWebhooks<\/th>\nBatch Export<\/th>\n<\/tr>\n<\/thead>\n
    Latency<\/td>\nReal-time (seconds)<\/td>\nDelayed (hours)<\/td>\n<\/tr>\n
    Completeness<\/td>\nMay miss events if endpoint is down<\/td>\nComplete dataset<\/td>\n<\/tr>\n
    Ordering<\/td>\nEvents may arrive out of order<\/td>\nOrdered by timestamp<\/td>\n<\/tr>\n
    Volume handling<\/td>\nMust handle bursts<\/td>\nProcess at your pace<\/td>\n<\/tr>\n
    Backfill<\/td>\nNot supported<\/td>\nRe-export any date range<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n
    Use webhooks for real-time reactions (fraud detection, instant notifications). Use batch export for analytics and reporting.<\/p>\n\n\n\n
    Data Warehouse Integration<\/h2>\n\n\n\n
    Loading into a Data Warehouse<\/h3>\n\n\n\n
    Once exported, load the data into your warehouse for joining with other business data:<\/p>\n\n\n\n
    -- BigQuery: Load from GCS\nLOAD DATA INTO analytics.deep_link_clicks\nFROM FILES (\n  format = 'CSV',\n  uris = ['gs:\/\/analytics-bucket\/deep-link-clicks\/2026-07-19.csv']\n);\n\n-- Snowflake: Load from S3\nCOPY INTO analytics.deep_link_clicks\nFROM @s3_stage\/deep-link-clicks\/2026-07-19.csv\nFILE_FORMAT = (TYPE = 'CSV' FIELD_DELIMITER = ',' SKIP_HEADER = 1);\n<\/code><\/pre>\n\n\n\n
    Joining with Business Data<\/h3>\n\n\n\n
    The real value of exported analytics is joining deep link data with business data:<\/p>\n\n\n\n
    -- Join deep link clicks with revenue data\nSELECT\n  dlc.campaign,\n  dlc.source,\n  COUNT(DISTINCT dlc.click_id) AS clicks,\n  COUNT(DISTINCT o.order_id) AS orders,\n  SUM(o.revenue) AS total_revenue,\n  ROUND(SUM(o.revenue) \/ COUNT(DISTINCT dlc.click_id), 2) AS revenue_per_click\nFROM deep_link_clicks dlc\nLEFT JOIN orders o\n  ON dlc.user_id = o.user_id\n  AND o.order_date BETWEEN dlc.timestamp AND dlc.timestamp + INTERVAL '7 days'\nWHERE dlc.timestamp >= '2026-07-01'\nGROUP BY dlc.campaign, dlc.source\nORDER BY total_revenue DESC;\n<\/code><\/pre>\n\n\n\n
    Schema Design<\/h3>\n\n\n\n
    Design your analytics tables for query performance:<\/p>\n\n\n\n
    CREATE TABLE deep_link_clicks (\n  click_id VARCHAR(32) PRIMARY KEY,\n  timestamp TIMESTAMP NOT NULL,\n  route VARCHAR(256),\n  source VARCHAR(64),\n  medium VARCHAR(64),\n  campaign VARCHAR(128),\n  platform VARCHAR(16),\n  os_version VARCHAR(16),\n  browser VARCHAR(64),\n  country CHAR(2),\n  city VARCHAR(128),\n  outcome VARCHAR(32),\n  latency_ms INTEGER,\n  referrer VARCHAR(512),\n  user_id VARCHAR(64),\n  -- Partition by date for query performance\n  date DATE GENERATED ALWAYS AS (CAST(timestamp AS DATE))\n);\n\n-- Index for common query patterns\nCREATE INDEX idx_clicks_campaign_date ON deep_link_clicks (campaign, date);\nCREATE INDEX idx_clicks_route_date ON deep_link_clicks (route, date);\nCREATE INDEX idx_clicks_platform_country ON deep_link_clicks (platform, country);\n<\/code><\/pre>\n\n\n\n
    Export Formats<\/h2>\n\n\n\n
    CSV vs JSON vs Parquet<\/h3>\n\n\n\n
    \n\n\n\n\n\n
    Format<\/th>\nSize (1M rows)<\/th>\nRead Speed<\/th>\nSchema<\/th>\nHuman Readable<\/th>\n<\/tr>\n<\/thead>\n
    CSV<\/td>\n~150MB<\/td>\nSlow<\/td>\nNo<\/td>\nYes<\/td>\n<\/tr>\n
    JSON<\/td>\n~300MB<\/td>\nMedium<\/td>\nPartial<\/td>\nYes<\/td>\n<\/tr>\n
    Parquet<\/td>\n~30MB<\/td>\nFast<\/td>\nYes<\/td>\nNo<\/td>\n<\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\n