dbt Incremental Strategy Understanding and Implementing the Power of Incremental Materializations

dbt is a powerful data transformation and modeling tool that allows you to work with large datasets by breaking them down into smaller, more manageable pieces. One of the most powerful features of dbt is its ability to handle incremental materializations — materializations that only include the most recent data necessary to keep your target tables in sync with your source data.

What is an Incremental Strategy?

An incremental strategy is a method that tells dbt to only process the new and modified data in your source tables. This means that if you have a table holding historical sales data, dbt will only update the latest sales figures in your target table, rather than deleting and reinserting the entire table on every run. As a result, incremental strategies can reduce processing costs and improve query performance.

Types of Incremental Strategies

dbt supports several types of incremental strategies, including:

1. Append: This strategy inserts the selected records from the source table into the destination table. It can't update or delete rows, just insert. Append is suitable when duplicates are not a concern.

2. Merge (With Clustered): This strategy solves the problem of duplicate records by checking if the unique key already exists in the destination table. If the unique key exists, the merge will update the record. If the records don't exist, the merge will insert them. A clustered destination table can improve merge performance and reduce costs.

3. Delete+insert: This strategy is similar to merge, but instead of updating existing records and inserting new records, it deletes existing records and inserts both new and existing records. It can generate duplicates if you do not set it right.

4. Insert+Overwrite (Partitioned): This strategy solves the problem of a full scan by working with partitions. For insert overwrite to perform the partition can be of the following types: date, datetime, timestamp, int64. However, if the timestamp is skewed, insert_overwrite is not an ideal solution. The insert_overwrite strategy deletes the selected partitions from the current destination table and inserts the selected transformed partitions into it.

Understanding Incremental Predicates

Incremental predicates is an advanced use of incremental models, where data volume is large enough to justify additional investments in performance. This config accepts a list of any valid SQL expression(s). dbt does not check the syntax of the SQL statements. This an example of a model configuration in a yml file:

models:
 - name: my_incremental_model
 config:
 materialized: incremental
 unique_key: id
 incremental_strategy: merge
 incremental_predicates: ["DBT_INTERNAL_DEST.session_start > dateadd(day, -7, current_date)"]

This will template (in the dbt.log file) a merge statement like:merge into DBT_INTERNAL_DEST from DBT_INTERNAL_SOURCE on — unique key DBT_INTERNAL_DEST.id = DBT_INTERNAL_SOURCE.id and — custom predicate: limits data scan in the "old" data / existing table DBT_INTERNAL_DEST.session_start > dateadd(day, -7, current_date) when matched then update … when not matched then insert …

How to Implement Incremental Strategies

Implementing incremental strategies in dbt involves adding a few lines of configuration to your model scripts. Here's how you can enable and use different incremental strategies:

1. Append: To enable the append strategy, simply add the materialized setting to incremental and add a conditional block for the is_incremental() macro:

models:
 - name: my_incremental_model
 config:
 materialized: incremental
 unique_key: id
 is_incremental(): "true"

2. Merge (With Clustered): To enable the merge strategy, you need to specify a unique key. Here's an example of how to set up a unique key for a table called orders:

models:
 - name: orders
 config:
 unique_key: order_id

Then, in your model script, you can enable the merge strategy by adding a conditional block for the is_incremental() macro:

# my_incremental_model.sql
{{ config(materialized='incremental', unique_key='order_id', is_incremental=True) }}

3. Delete+_insert: To enable the delete+insert strategy, you can leave the materialized setting to incremental, but you also need to set the merge_strategy setting to 'delete+insert'. Here's an example of how to set up a unique_key for a table called employees:

models:
 - name: employees
 config:
 unique_key: employee_id

Then, in your model script, you can enable the delete+insert strategy by adding a conditional block for the is_incremental() macro:

# employees.sql
{{ config(materialized='incremental', unique_key='employee_id', merge_strategy='delete+insert') }}

4. Insert+Overwrite (Partitioned): To enable the insert+overwrite strategy, you need to set the materialized setting to 'incremental' and specify a partitions config. Here's an example of how to set up a unique_key for a table called sales_data:

models:
 - name: sales_data
 config:
 materialized: incremental
 unique_key: date_sold

Then, in your model script, you can enable the insert+overwrite strategy by adding a conditional block for the is_incremental() macro:

# sales_data.sql
{{ config(materialized='incremental', unique_key='date_sold', partitions=lambda: {"field": "date_sold", "start": "2022-01-01", "end": "2022-01-05"}}) }}

Summary

Increasingly, organizations are turning to big data solutions like dbt to help them manage and analyze their massive datasets. dbt's incremental strategies offer a powerful way to keep their data tables in sync, reduce costs, and improve performance. This guide provides an overview of different incremental strategies, how they work, and how to implement them in your dbt models. Whether you're a beginner or an experienced dbt user, understanding and utilizing incremental strategies can help you harness the full potential of your data transformation projects.