Incremental models in DBT are a type of materialization designed to optimize data transformation pipelines, especially for large datasets where rebuilding the entire table from scratch would be time-consuming or resource-intensive. Instead of recreating the table each time the model is run, incremental models process only new or updated records and append or update them in the destination table.
Incremental models help optimize data transformation pipelines in the following ways:
- Reduced processing time: By only processing new or updated records, incremental models can significantly reduce the time it takes to update the destination table compared to rebuilding it from scratch.
- Lower resource consumption: Incremental models reduce the computational resources required for data transformation as they only work on a smaller subset of the data, rather than the entire dataset.
- Faster updates: By processing only new or updated records, incremental models can provide quicker updates to downstream systems that rely on the transformed data.
- Minimized data duplication: Incremental models help minimize data duplication by only adding or updating records in the destination table, avoiding the need to store multiple copies of the same data.
Example:
Let’s consider an example to illustrate how incremental models work and optimize data transformation pipelines. Assume you have an e-commerce platform that records user events, such as page views, clicks, and purchases, in a raw events table. You want to create a transformed table that stores the total number of events per user per day.
Without incremental models, you would have to rebuild the entire transformed table each time new events are recorded. This process would be inefficient and time-consuming, especially if the raw events table is large and grows constantly.
By using incremental models, you can process only new or updated records, reducing the processing time and resources required. Here’s how to set up an incremental model in DBT:
- In the model’s SQL file, add a
configblock with the materialization set to ‘incremental’:
{{
config(
materialized='incremental'
)
}}
2. Write the SQL query that calculates the total number of events per user per day for new or updated records. Use the dbt_utils.surrogate_key function to generate a unique key for each record, based on user ID and date.
SELECT
dbt_utils.surrogate_key(user_id, date) AS event_key,
user_id,
date,
COUNT(*) as event_count
FROM raw_events
-- Only process records that are newer than the latest record in the destination table
WHERE date >= (SELECT MAX(date) FROM {{ this }})
GROUP BY user_id, date
3. In the model’s schema.yml file, add a unique key configuration to tell DBT how to identify and update existing records:
models:
- name: my_incremental_model
config:
materialized: incremental
columns:
- name: event_key
tests:
- unique
- not_null
Now, when you run the DBT model, it will only process new or updated records from the raw_events table, appending or updating them in the destination table. This approach significantly reduces the processing time and resources required, optimizing your data transformation pipeline.
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