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:

AggregationDescription
YearExtracts the 4-digit year (e.g., 2024)
QuarterExtracts the calendar quarter (Q1–Q4)
MonthExtracts the month name or number
Week NumberExtracts the ISO week number of the year
Week DayExtracts the weekday name (Monday, Tuesday, etc.)
DateExtracts just the calendar date (YYYY-MM-DD)
Day NumberExtracts the day of the month (1–31)
TimeExtracts just the time portion (HH:MM:SS)
HourExtracts the hour from the datetime (0–23)
MinuteExtracts the minute component (0–59)
SecondExtracts the second component (0–59)

How to Aggregate a Datetime Column in Edilitics

  1. Select the datetime column

    Only eligible datetime, date, timestamp, or time fields will appear. If none exist, you’ll be notified.

  2. Choose the aggregation type

    Select from any of the supported options listed above.

  3. Review the preview

    A preview of the new values will appear, based on the selected transformation.

  4. 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.

  5. 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_SaleDate
FROM sales_data;

Pandas Example


df['Month_SaleDate'] = pd.to_datetime(df['sale_date']).dt.month
df['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

ScenarioUse Case
Retail – SaleDate → MonthTrack monthly sales to identify seasonal trends and optimize inventory.
Finance – TransactionDate → DayMonitor daily financial activity and detect spikes in transaction volume.
Manufacturing – StartTime → HourAnalyze machine usage across work shifts for operational improvements.
Healthcare – AdmissionDate → WeekStudy weekly admission patterns to improve staffing and planning.
Education – ExamDate → QuarterCompare 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

For technical inquiries, implementation support, or enterprise-level assistance, our dedicated technical support team is available to ensure optimal deployment and utilization of Edilitics solutions. Please contact our enterprise support desk at support@edilitics.com. Our team of specialists will respond promptly to address your requirements.

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