Datetime Aggregations | Summarize Temporal Trends Without Code
Time-based patterns often reveal the most critical insights—but extracting them manually from timestamp columns can be slow, error-prone, and code-heavy.
Edilitics solves this by offering a no-code Datetime Aggregations operation that lets you extract granular time units from any date, time, or timestamp column—with automatic column naming and full schema validation built in.
Why Aggregate by Time?
Datetime fields are central to understanding:
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Sales trends over months or quarters
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User activity by hour or day
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Operational bottlenecks during specific time windows
But in most tools, deriving these insights requires DATE_TRUNC()
logic or custom scripts.
With Edilitics, time-based groupings become accessible through a visual interface that:
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✅ Detects eligible date and time fields
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✅ Offers granular options like Year, Quarter, Week, Hour, etc.
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✅ Auto-generates a new column using a smart naming convention (e.g.,
Month_SaleDate
) -
✅ Shows a real-time preview before execution
Supported Aggregation Types
Each aggregation extracts a specific component of the datetime field:
Aggregation | Description |
---|---|
Year | Extracts the 4-digit year (e.g., 2024 ) |
Quarter | Extracts the calendar quarter (Q1 –Q4 ) |
Month | Extracts the month name or number |
Week Number | Extracts the ISO week number of the year |
Week Day | Extracts the weekday name (Monday , Tuesday , etc.) |
Date | Extracts just the calendar date (YYYY-MM-DD ) |
Day Number | Extracts the day of the month (1–31 ) |
Time | Extracts just the time portion (HH:MM:SS ) |
Hour | Extracts the hour from the datetime (0–23 ) |
Minute | Extracts the minute component (0–59 ) |
Second | Extracts the second component (0–59 ) |
How to Aggregate a Datetime Column in Edilitics
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Select the datetime column
Only eligible datetime, date, timestamp, or time fields will appear. If none exist, you’ll be notified.
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Choose the aggregation type
Select from any of the supported options listed above.
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Review the preview
A preview of the new values will appear, based on the selected transformation.
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New column auto-naming
Edilitics auto-names the resulting column by combining the aggregation and source field (e.g.,
Month_SaleDate
). You can customize the name if preferred. -
Run the operation
Execute to apply the transformation and generate the new column.
Manual Equivalent – SQL & Pandas Examples
Here’s how this operation would typically be done via code:
SQL Example – Redshift
SELECT EXTRACT(MONTH FROM sale_date) AS Month_SaleDate, EXTRACT(YEAR FROM sale_date) AS Year_SaleDateFROM sales_data;
Pandas Example
df['Month_SaleDate'] = pd.to_datetime(df['sale_date']).dt.monthdf['Year_SaleDate'] = pd.to_datetime(df['sale_date']).dt.year
Edilitics enables the same result through a point-and-click interface—no scripting required.
Common Use Cases for Datetime Aggregations
Scenario | Use Case |
---|---|
Retail – SaleDate → Month | Track monthly sales to identify seasonal trends and optimize inventory. |
Finance – TransactionDate → Day | Monitor daily financial activity and detect spikes in transaction volume. |
Manufacturing – StartTime → Hour | Analyze machine usage across work shifts for operational improvements. |
Healthcare – AdmissionDate → Week | Study weekly admission patterns to improve staffing and planning. |
Education – ExamDate → Quarter | Compare student performance across academic terms and sessions. |
Smart, Safe, and Schema-Aware
Datetime aggregations in Edilitics are:
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✅ Type-validated – Only compatible columns can be selected
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✅ Error-guarded – Invalid fields or empty results are flagged instantly
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✅ Auto-named – Output columns follow a logical naming structure
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✅ Previewable – See sample output before committing the change
Time-based analysis is core to any data-driven decision. With Datetime Aggregations, Edilitics enables users to segment and summarize datasets across granular time units—from years to minutes—with zero code. Built-in validation and auto-type filtering ensure you're always working with clean, temporal logic—backed by governed execution at scale.
Next: Build On Your Aggregations
Once your time-based grouping is set up, you're ready to explore powerful combinations like:
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