Examples: Validating Data Freshness

Validating Data Freshness

Use date/datetime-based validations to ensure your data is current and recent. This is critical for applications that depend on timely data updates.

		STEP	COLUMNS	VALUES	EVAL	UNITS	PASS	FAIL	W	E	C	EXT
Pointblank Validation
2025-10-09\|20:53:32 Polars
#4CA64C	1	specially() Recent data available (within 2 days of 2023-12-31)		EXPR	✓	1	1 1.00	0 0.00	—	—	—	—
#4CA64C	2	col_vals_ge() All data points are from the last week	data_timestamp	2023-12-24	✓	4	4 1.00	0 0.00	—	—	—	—
#4CA64C	3	specially() Most recent data is from today		EXPR	✓	1	1 1.00	0 0.00	—	—	—	—
#4CA64C	4	col_vals_not_null() No missing timestamps	data_timestamp	—	✓	4	4 1.00	0 0.00	—	—	—	—
2025-10-09 20:53:32 UTC< 1 s2025-10-09 20:53:32 UTC

import pointblank as pb
import polars as pl
from datetime import date, datetime, timedelta

# Create sample data with mixed freshness levels
freshness_data = pl.DataFrame({
    "data_timestamp": [
        datetime(2023, 12, 28, 10, 30),  # 3 days ago from Dec 31
        datetime(2023, 12, 29, 14, 15),  # 2 days ago
        datetime(2023, 12, 30, 9, 45),   # 1 day ago
        datetime(2023, 12, 31, 16, 20),  # Today
    ],
    "sensor_id": ["TEMP_01", "TEMP_02", "TEMP_01", "TEMP_03"],
    "reading": [22.5, 21.8, 23.1, 22.9],
    "quality_score": [0.95, 0.88, 0.92, 0.97]
})

# Assuming today is 2023-12-31, check for data freshness
current_date = date(2023, 12, 31)
freshness_cutoff = current_date - timedelta(days=2)  # Data should be within 2 days

validation = (
    pb.Validate(freshness_data)
    .specially(
        expr=lambda df: df.filter(
            pl.col("data_timestamp").dt.date() >= freshness_cutoff
        ).height > 0,
        brief=f"Recent data available (within 2 days of {current_date})"
    )
    .col_vals_ge(
        columns="data_timestamp",
        value=current_date - timedelta(days=7),  # Within last week
        brief="All data points are from the last week"
    )
    .specially(
        expr=lambda df: (
            df.select(pl.col("data_timestamp").max()).item().date() >= current_date
        ),
        brief="Most recent data is from today"
    )
    .col_vals_not_null(
        columns="data_timestamp",
        brief="No missing timestamps"
    )
    .interrogate()
)

validation

Preview of Input Table

	data_timestamp Datetime	sensor_id String	reading Float64	quality_score Float64
PolarsRows4Columns4
1	2023-12-28 10:30:00	TEMP_01	22.5	0.95
2	2023-12-29 14:15:00	TEMP_02	21.8	0.88
3	2023-12-30 09:45:00	TEMP_01	23.1	0.92
4	2023-12-31 16:20:00	TEMP_03	22.9	0.97