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:
-
Sales trends over months or quarters
-
User activity by hour or day
-
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:
-
Detects eligible date and time fields
-
Offers granular options like Year, Quarter, Week, Hour, etc.
-
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
-
Select the datetime column
Only eligible datetime, date, timestamp, or time fields will appear. If none exist, you’ll be notified.
-
Choose the aggregation type
Select from any of the supported options listed above.
-
Review the preview
A preview of the new values will appear, based on the selected transformation.
-
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:
-
Type-validated – Only compatible columns can be selected
-
Error-guarded – Invalid fields or empty results are flagged instantly
-
Auto-named – Output columns follow a logical naming structure
-
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:
Enterprise Support & Technical Assistance