Handling late-arriving data in dbt

Introduction

Sometimes data comes late or out of order. Handling late-arriving data helps keep your reports accurate and up-to-date.

When sales data arrives a day or more after the sale happened.

When customer updates come after your daily report runs.

When sensor data is delayed due to network issues.

When you want to correct past data without breaking your reports.

Syntax

dbt

with source_data as (
    select * from {{ ref('raw_table') }}
),
updated_data as (
    select *,
        case
            when event_date < current_date - interval '1 day' then 'late'
            else 'on_time'
        end as data_status
    from source_data
)
select * from updated_data

Use case statements to flag late data.

Use ref() to refer to other dbt models or tables.

Examples

Flags events that arrived more than 1 hour late.

dbt

select *,
    case
        when event_timestamp < current_timestamp - interval '1 hour' then 'late'
        else 'on_time'
    end as data_status
from {{ ref('events') }}

Selects orders that arrived late by more than 2 days.

dbt

with late_data as (
    select * from {{ ref('orders') }}
    where order_date < current_date - interval '2 days'
)
select * from late_data

Sample Program

This dbt model flags sales as 'late' if they arrived more than one day after the sale date. It helps identify and handle late-arriving sales data.

dbt

with raw_sales as (
    select * from {{ ref('sales_raw') }}
),
flagged_sales as (
    select *,
        case
            when sale_date < current_date - interval '1 day' then 'late'
            else 'on_time'
        end as arrival_status
    from raw_sales
)
select sale_id, sale_date, arrival_status from flagged_sales order by sale_date

Important Notes

Late-arriving data can cause reports to be wrong if not handled.

Flagging late data helps you decide how to update or ignore it.

Use dbt's incremental models to update only changed or late data efficiently.

Summary

Late-arriving data means data that comes after expected time.

Flag or filter late data to keep your analysis accurate.

dbt helps manage late data with SQL and incremental models.

Practice

1. What does late-arriving data mean in data processing?

easy

A. Data that is generated by a different system

B. Data that arrives after the expected time window

C. Data that is duplicated in the dataset

D. Data that is corrupted or incomplete

Solution

1. Step 1: Understand the timing of data arrival

   Late-arriving data refers to data that comes in after the time it was expected to be available for processing.

2. Step 2: Differentiate from other data issues

   It is not about corruption, duplication, or source system differences, but purely about timing.

3. Final Answer:

   Data that arrives after the expected time window -> Option B

4. Quick Check:

   Late-arriving data = late arrival [OK]

Hint: Late-arriving means data comes late, not corrupted or duplicated [OK]

Common Mistakes:

• Confusing late data with corrupted data
• Thinking late data means duplicate records
• Assuming late data is from a different system

2. Which of the following is the correct way to filter late-arriving data in a dbt SQL model?

easy

A. WHERE event_date < current_date - interval '1 day'

B. WHERE event_date > current_date + interval '1 day'

C. WHERE event_date = current_date

D. WHERE event_date IS NULL

Solution

1. Step 1: Understand filtering late data by date

   Late-arriving data is usually older than the current date minus a buffer (like 1 day), so we filter for dates less than that.

2. Step 2: Check each option's logic

   WHERE event_date < current_date - interval '1 day' filters for event_date before yesterday, which captures late data. WHERE event_date > current_date + interval '1 day' filters future dates, which is incorrect. WHERE event_date = current_date filters only today's data, ignoring late data. WHERE event_date IS NULL filters nulls, unrelated to late data.

3. Final Answer:

   WHERE event_date < current_date - interval '1 day' -> Option A

4. Quick Check:

   Filter dates before yesterday = WHERE event_date < current_date - interval '1 day' [OK]

Hint: Late data usually has older dates than today minus buffer [OK]

Common Mistakes:

• Filtering future dates instead of past dates
• Filtering only current date data
• Filtering null dates instead of late data

3. Given this dbt incremental model SQL snippet:

SELECT
  id,
  event_date,
  data_value
FROM source_table
WHERE event_date >= (SELECT MAX(event_date) FROM {{ this }})

What is the main purpose of this WHERE clause?

medium

A. To select only new or updated data since last run

B. To select all data including duplicates

C. To exclude all late-arriving data

D. To select data with null event_date

Solution

1. Step 1: Analyze the WHERE clause logic

   The clause filters rows where event_date is greater or equal to the max event_date already in the target table, meaning it picks new or updated data since last run.

2. Step 2: Understand incremental model behavior

   This is a common pattern in dbt incremental models to avoid reprocessing old data and only add new or changed rows.

3. Final Answer:

   To select only new or updated data since last run -> Option A

4. Quick Check:

   Incremental filter = new or updated data [OK]

Hint: Incremental models filter data newer than max existing date [OK]

Common Mistakes:

• Thinking it excludes late-arriving data
• Assuming it selects duplicates
• Confusing null filtering with this clause

4. You wrote this dbt incremental model filter:

WHERE event_date > (SELECT MAX(event_date) FROM {{ this }})

But your model misses some late-arriving data. What is the likely problem?

medium

A. The subquery syntax is invalid in dbt

B. You should filter with event_date < instead

C. Using > instead of >= excludes data with event_date equal to max date

D. Late-arriving data always has null event_date

Solution

1. Step 1: Understand the difference between > and >=

   Using > excludes rows where event_date equals the max date already processed, which can miss late-arriving data with that date.

2. Step 2: Correct the filter to include equal dates

   Changing to >= includes those rows, capturing late-arriving data that arrived late but has the same event_date.

3. Final Answer:

   Using > instead of >= excludes data with event_date equal to max date -> Option C

4. Quick Check:

   Use >= to include late data on max date [OK]

Hint: Use >= not > to include late data on max date [OK]

Common Mistakes:

• Using > excludes late data with max date
• Thinking subquery is invalid in dbt
• Filtering with < instead of >
• Assuming late data has null dates

5. You want to handle late-arriving data in a dbt incremental model. Which approach best ensures late data is included without duplicating existing rows?

hard

A. Filter only event_date = current_date to avoid duplicates

B. Delete all data and reload full dataset every run

C. Ignore late-arriving data to keep model simple

D. Use a merge strategy with unique keys and filter event_date >= max date

Solution

1. Step 1: Understand the goal of including late-arriving data

   Late data can have event_dates already processed, so we must update or insert without duplicates.

2. Step 2: Use merge strategy with unique keys and proper filtering

   Using a merge (upsert) with unique keys and filtering event_date >= max date ensures late data is added or updated correctly.

3. Step 3: Evaluate other options

   Deleting all data every run is costly. Ignoring late data loses accuracy. Filtering only current_date misses late data.

4. Final Answer:

   Use a merge strategy with unique keys and filter event_date >= max date -> Option D

5. Quick Check:

   Merge + unique keys + >= max date = best late data handling [OK]

Hint: Merge with unique keys and >= max date handles late data well [OK]

Common Mistakes:

• Reloading full data every run is inefficient
• Ignoring late data causes incomplete analysis
• Filtering only current date misses late data