Transform a changelog in a daily changelog with an incremental model

I would like to make an incremental DBT model of a daily changelog from a changelog that looks like the following:

    id1 2023-01-01 13:40 data1_v1 create
    id1 2023-01-01 15:00 data1_v2 update
    id1 2023-01-03 00:02 data1_v3 update
    id1 2023-01-04 05:04 data1_v3 delete
    id2 2023-01-01 XX:XX data2_v1 create
    id2 2023-01-02 XX:XX data2_v2 update
    id2 2023-01-04 XX:XX data2_v3 update

I would like to accomplish a daily picture of the data of each id (filtering deletes each day), so we get a daily changelog:

    ID  DATE       DATA    
    id1 2023-01-01 data1_v2
    id1 2023-01-02 data1_v2
    id1 2023-01-03 data1_v3
    id2 2023-01-01 data2_v1
    id2 2023-01-02 data2_v2
    id2 2023-01-03 data2_v2
    id2 2023-01-04 data2_v3


  • For id1 in date 2023-01-01, data1_v2 is the data we want because it is the latest that day
  • id1 does not figure in 2023-01-04 because it was deleted
  • id2 in date 2023-01-03 is data2_v2 since the it didn't change that day

We use BigQuery as our warehouse connected to DBT.

My current approach is the following:

    days as (
        select day
        from unnest(generate_date_array(date_sub(current_date(), interval 6 month), current_date())) day
    changelog as (
        select date, timestamp, document_id, data
        from {{ ref("base_firestore_export__event_raw_changelog") }}
    daily_changelog as (
            last_value(data ignore nulls) over (order by timestamp) as data
        from days d
        left join changelog c on =
        {% if is_incremental() %}
            -- this filter will only be applied on an incremental run
            where date > (select max(date) from {{ this }}) 
        {% endif %}
select *
from daily_changelog

but I get a memory error:

Database Error in rpc request (from remote system) Resources exceeded during query execution: The query could not be executed in the allotted memory. Peak usage: 122% of limit. Top memory consumer(s): sort operations used for analytic OVER() clauses: 98% other/unattributed: 2%

Any help would be much appreciated! Thanks!

In general, this means that one worker got a lot of workload. Based on your query this line could be the reason why last_value(data ignore nulls) over (order by timestamp) as data

Readmore: Resources exceeded BigQuery - Stack Overflow