🎯 Step-by-Step Tutorial
Step 1: Load Sample Data
Click "📋 Load Sample" to see how the aggregator works with realistic sales data from multiple sources.
{"id": 1, "name": "John", "sales": 15000, "region": "North", "date": "2025-01-15"}
Step 2: Configure Merge Strategy
Choose how to combine your data sources:
- Concatenate: Combine all arrays into one
- Union by Key: Merge records with same unique identifier
- Intersection: Keep only common records
Step 3: Set Up Grouping
Add group by fields like "region" or "department" to organize your data. Use the "+ Add Group Field" button to create multiple grouping levels.
Step 4: Choose Statistical Functions
Select which calculations to perform (sum, average, count, etc.) and specify which fields to aggregate (e.g., "sales, revenue").
💼 Real-World Use Cases
🏢 Sales Performance Analysis
Scenario: You have sales data from multiple regions and quarters that need to be consolidated for executive reporting.
Solution: Use concatenate merge strategy, group by region and quarter, aggregate sales with sum and average functions. Enable time-based aggregation for monthly trends.
Result: Comprehensive sales report showing totals, averages, and trends by region and time period.
📊 User Analytics Consolidation
Scenario: Combine user activity data from web app, mobile app, and API usage to understand overall engagement.
Solution: Use union by key (user_id), group by platform and activity type, calculate count, sum, and average for engagement metrics.
Result: Unified view of user behavior across all platforms with detailed engagement statistics.
💰 Financial Data Reconciliation
Scenario: Monthly financial data from different departments needs to be reconciled with automatic conflict resolution for discrepancies.
Solution: Use merge strategy with sum conflict resolution, group by department and cost center, aggregate budget and actual spending with variance calculations.
Result: Reconciled financial report with automatic conflict resolution and variance analysis.
❓ Frequently Asked Questions
What's the difference between merge strategies?
Concatenate: Simply combines all data into one array. Merge: Combines objects with conflict resolution. Union: Merges by unique key, keeping all records. Intersection: Keeps only records that exist in all sources.
How does conflict resolution work?
When the same field exists in multiple sources, you can choose to use the first value, last value, sum numbers, take the average, or use min/max values. The tool will show warnings about resolved conflicts.
Can I aggregate nested object properties?
Yes! Use dot notation for nested properties (e.g., "user.profile.age" or "metrics.sales.total"). The tool automatically detects and lists available nested fields.
What date formats are supported for time-based aggregation?
The tool supports ISO dates (2025-01-15), timestamps, and most common date formats. It automatically parses dates and groups them by day, week, month, quarter, or year.
How large datasets can I process?
The tool is optimized for datasets with thousands of records. For very large files (>10MB), consider splitting your data or using the streaming features in other JSON tools for better performance.