Understanding dbt. Incremental materialization

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Welcome back to the Understanding dbt series! If you’ve been following along, this is the third article in our journey through dbt, where we dive into various aspects of this powerful data transformation tool. In case you missed them, feel free to check out the previous articles:

• Understanding dbt: Models

• Understanding dbt: Introduction to the T of the Modern Data Stack

In today’s article, we’ll explore incremental materialization in dbt — a method for handling large datasets more efficiently by processing only the new or changed data. Incremental materialization is a crucial concept when working with big data, as it enables faster processing and minimizes storage needs, especially when dealing with data that grows over time.

We’ll walk through what incremental materialization is, when and why it’s useful, and how to set it up in dbt. By the end of this article, you’ll have a solid understanding of implementing incremental models to optimize your data workflows.

Let’s get started!

What is Incremental Materialization?

In dbt, materializations control how the results of your models are stored in your data warehouse. While you may already be familiar with basic materializations like view and table, incremental materialization offers a unique advantage when dealing with large datasets.

Understanding dbt. Models

Incremental materialization is a technique that allows dbt to process only the new or modified data rather than rebuilding the entire table every time the model runs. This can be a game-changer when working with continuously growing datasets, such as event logs or transactional data. Instead of reprocessing millions of rows, dbt only adds or updates what’s changed, saving both time and computational resources.

Why Use Incremental Materialization?

Incremental materialization is particularly useful in scenarios where:

• The dataset is too large to fully rebuild every time.
• Only a small subset of the data changes frequently (e.g., daily or hourly).
• Processing time and resource efficiency are critical, especially with big data.

By using incremental models, you not only speed up processing times but also reduce the load on your data warehouse, making it a cost-effective and scalable approach for handling growing datasets.

How Incremental Models Work in dbt

The underlying mechanics can vary depending on the capabilities of your data warehouse, especially regarding support for merge statements and transaction management.

Merge Strategy

For databases that support merge statements, dbt leverages this capability to handle incremental loads efficiently. A merge statement allows dbt to insert new records and update existing ones in a single operation. This is the most efficient approach because it minimizes the need for multiple steps and reduces the processing time.

• Supported Warehouses: On databases like Snowflake, BigQuery, and others that support merge, dbt can seamlessly handle both insertions and updates without needing to delete records manually.
• How it Works: dbt compares new records from the source data with existing records in the target table based on the unique identifier or unique_key. It then updates rows where there are changes and inserts new rows that don’t already exist in the table.

MERGE INTO target_table AS target
USING source_table AS source
ON target.id = source.id
WHEN MATCHED THEN
    UPDATE SET target.value = source.value
WHEN NOT MATCHED THEN
    INSERT (id, value) VALUES (source.id, source.value)

Delete-and-Insert Strategy

For data warehouses that don’t support merge statements, dbt implements incremental updates by first using a delete statement to remove records in the target table that need to be updated. After the deletion, dbt runs an insert statement to add both new and updated records.

• Unsupported Warehouses: For databases without merge support, dbt relies on the delete-and-insert approach.
• How it Works: dbt identifies records that need updating, deletes them from the target table, and then inserts the new or updated records. While effective, this approach may not be as efficient as a merge statement, as it involves two separate steps, which can be more resource-intensive.

DELETE FROM target_table
WHERE id IN (SELECT id FROM source_table);

INSERT INTO target_table (id, value)
SELECT id, value FROM source_table;

Transaction Management

dbt uses transaction management to ensure the integrity of your data during incremental updates, particularly on databases that support transactions. Transaction management treats a set of actions as a single unit of work (or transaction), ensuring that either all actions succeed or none at all. If any part of the incremental update fails, dbt will roll back the entire transaction, restoring the database to a consistent state.

Why It Matters: Transaction management provides a safeguard, especially when working with complex incremental models that involve multiple steps. By ensuring that either the entire update succeeds or fails as a whole, dbt helps protect data integrity, reducing the risk of errors affecting your dataset.

Key Concepts for Incremental Materialization

To effectively set up incremental materialization in dbt, there are a few important concepts to understand. These ideas help you configure your model to process only the new or changed data and ensure that your dataset remains accurate and up-to-date.

1. Unique Identifiers

Each row in your dataset should have a unique identifier, such as a primary key or unique field, to track and manage data changes. This unique identifier allows dbt to differentiate between new, existing, and updated records. It’s essential because incremental logic relies on these identifiers to avoid duplicate entries and accurately update the dataset.

For a transactional dataset, a column like transaction_id or order_id would serve as a unique identifier.

2. Change Tracking

To perform incremental updates, you need a way to identify which records have changed since the last run. Typically, this is done by using a timestamp column that records when a row was last updated, like updated_at or last_modified. By filtering rows based on this timestamp, you can tell dbt to process only the records that are new or modified.

Types of Columns Suitable for Incremental Loading:

• Timestamp Columns: These are the most commonly used for incremental loading. A column like updated_at or last_modified is ideal because it records the exact time a row was created or updated. With this column, dbt can efficiently filter for new or modified rows since the last run.

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

• Sequence Columns: In cases where timestamps aren’t available, a monotonically increasing sequence column (e.g., an id that auto-increments with each new row) can be used. This allows dbt to filter for rows with ids greater than the highest id currently in the table. However, this method is less precise because it doesn’t capture updates to existing rows, only new additions.

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

Choosing the right type of column for incremental loading depends on the nature of your data and whether you need to capture both new and modified records or only new additions.

3. Incremental Logic

The incremental logic is where the magic happens in dbt. This logic determines what qualifies as “new” data and how it should be handled. In dbt, you can use the is_incremental() function to write conditional logic that only runs when the model is in incremental mode.

This allows you to create filters and conditions specifically for incremental runs, optimizing your data processing.

For instance, you can use the is_incremental() function to only include rows where updated_at is greater than the maximum updated_at value currently in the dataset. This ensures that only new or updated records are added to the table.

-- Example of incremental logic in a dbt model
{% if is_incremental() %}
    WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}

Step-by-Step Guide to Implementing Incremental Materialization

Now that we understand the key concepts behind incremental materialization, let’s dive into the actual implementation. In this section, we’ll go through a step-by-step guide to creating an incremental model in dbt, complete with example code snippets.

1. Creating an Incremental Model

The first step is to create a model file in your dbt project’s models folder. In this file, you’ll define the SQL logic for your model and configure it for incremental materialization.

Step 1: Create a new SQL file in the models folder, such as my_incremental_model.sql.

Step 2: At the top of the file, specify that this model should be materialized incrementally by adding a config block.

{{ config(
    materialized='incremental'
) }}

This configuration tells dbt that you want to use incremental materialization for this model.

By setting materialized=’incremental’, dbt knows that it should only process new or modified data rather than reprocessing the entire dataset every time.

This is particularly useful for large datasets where a full refresh would be inefficient.

2. Using the is_incremental() Function

The is_incremental() function in dbt is essential for defining the logic that should be applied during incremental loads. It allows you to add a conditional WHERE clause that will only execute when the model is in incremental mode.

Let’s assume you have a timestamp column named updated_at to track changes in your dataset. Here’s how you can use is_incremental() to filter for only new or modified records:

SELECT *
FROM my_source_table
{% if is_incremental() %}
    WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}

The is_incremental() function checks if the model is running in incremental mode. When is_incremental() returns true, dbt applies the WHERE clause, which filters for rows where updated_at is greater than the maximum updated_at value in the existing table.

This ensures that only new or updated rows are included in the load, rather than reloading the entire dataset. This filtering saves processing time by only processing data that has changed.

3. Handling Different Scenarios in Incremental Models

Incremental materialization can be adapted for different scenarios based on the specific needs of your data. Here are two common scenarios and how to handle them:

Appending New Data Only: If you only want to append new records without updating existing rows, the above code works perfectly. The WHERE clause ensures only new data is added, making it ideal for growing datasets like log files.

In this scenario, incremental loading will only add new records that were not in the previous load, making it efficient for cases where data is continuously added but existing data is not modified.

Updating Changed Records: If you also need to update modified records, you’ll have to adjust your incremental logic. This can be done by incorporating additional conditions in the SQL query or using the unique_key configuration.

{{ config(
    materialized='incremental',
    unique_key='transaction_id'
) }}

SELECT *
FROM my_source_table
{% if is_incremental() %}
    WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}

By setting a unique_key, dbt ensures that each row is unique based on this key. When new data with an existing transaction_id is detected, dbt will update the existing row instead of creating duplicates, which is useful for data that may be modified after the initial load.

4. Running the Incremental Model

Once your incremental model is configured and your logic is set up, you’re ready to run the model. Use the following command:

dbt run --select my_incremental_model

This command runs the specified incremental model. The first time the model runs, it will build the entire table from scratch. On subsequent runs, thanks to the incremental configuration, dbt will only process new or updated records, improving performance and saving resources.

Handling Edge Cases in Incremental Materialization

While incremental materialization can be a powerful way to optimize data processing, there are some edge cases that you need to consider to ensure accuracy and reliability.

Here are a few common challenges and strategies for handling them effectively.

1. Handling Deleted Records

In incremental models, new and updated rows are added to the target table, but deletions can pose a challenge. If a row is deleted in the source data, it won’t automatically be removed from the target table in an incremental model. There are a few ways to handle this:

Soft Deletes: Add a column in your source data, like is_deleted, to mark rows as deleted. In your incremental model, you can filter out rows where is_deleted is set to true, which effectively “removes” them from active results without deleting them from the table.

Periodic Full Refresh: Schedule a full refresh of the model periodically, such as weekly or monthly, to rebuild the table from scratch. This ensures that any deleted rows are removed from the final dataset. To run a full refresh, use the command:

dbt run --full-refresh --select my_incremental_model

2. Handling Overlapping Data or Duplicates

Incremental models rely on unique identifiers and change tracking to identify new and modified records. However, if there’s an overlap between old and new data, or if duplicates are introduced, it can lead to inaccurate results. Here’s how to address this:

Deduplication Logic: Use a unique key (e.g., transaction_id or order_id) to identify and remove duplicate entries. Setting a unique_key in dbt’s configuration helps prevent duplicates by updating existing rows rather than inserting new ones.

{{ config(
    materialized='incremental',
    unique_key='transaction_id'
) }}

Use Aggregation: If applicable, aggregate data in a way that minimizes duplicates. For example, if you are tracking daily totals, ensure your query logic groups by date or another relevant identifier.

3. Managing Schema Changes

Schema changes in the source data, such as added columns or data type changes, can affect the behavior of your incremental model. Since dbt does not automatically handle schema changes, here are some strategies to manage them:

Column Renames: If you need to rename a column, update both the source and model SQL. Running a full refresh afterward ensures that your table reflects the schema change without conflicts.

New Columns: When adding a new column to your source data, ensure it’s also added to the model. If the column is optional, you may add a default value in your SQL logic to handle cases where it’s missing.

Data Type Changes: When changing data types, it’s best to run a full refresh to avoid issues where new data types conflict with existing data. For example, switching from int to string without a full refresh could lead to data inconsistencies.

4. Ensuring Data Consistency

Data consistency is essential in incremental models, especially when relying on timestamps or unique identifiers. Here are some tips for maintaining consistency:

Validate Incremental Logic: Regularly review and validate your incremental logic to ensure it accurately captures all relevant changes in your source data. Incorrect filtering or conditions in is_incremental() can result in missing or duplicate records.

Monitor Execution Logs: Check dbt’s execution logs for any unexpected issues or patterns. For example, if the number of processed rows drops suddenly, it might indicate a problem in your incremental logic or data pipeline.

Use dbt Tests: dbt provides testing capabilities to validate data integrity. Add tests to your model to check for duplicates, nulls, and other potential issues affecting data consistency.

Performance Tips for Incremental Materialization

One of the biggest advantages of using incremental materialization in dbt is the potential for improved performance, especially when working with large datasets. However, to get the most out of incremental models, there are some additional optimizations you can apply. Here are a few tips to help you maximize the efficiency of your incremental models.

1. Use Partitioning and Clustering

Partitioning and clustering are techniques that allow you to organize data within your table, improving query performance. Depending on your data warehouse, these methods can significantly speed up queries by limiting the amount of data processed.

• Partitioning: Organize your data into partitions, such as by date, to limit the number of rows scanned during each incremental load. For example, partitioning by date or month allows the database to only process the relevant partitions, making queries faster.

{{ config(
    materialized='incremental',
    partition_by={'field': 'date', 'data_type': 'date'}
) }}

• Clustering: If your data warehouse supports clustering, you can group similar data together in a way that improves query efficiency. Clustering works well for fields that are frequently filtered or aggregated in your queries.

2. Minimize the Amount of Data Processed

When setting up incremental models, it’s important to limit the amount of data being processed during each run. This can be achieved by optimizing your incremental logic and using efficient filters.

• Optimized Incremental Logic: Make sure your is_incremental() logic only includes the necessary data. For example, if you’re tracking updates by updated_at, use a precise filter to exclude older records.

{% if is_incremental() %}
    WHERE updated_at > (SELECT MAX(updated_at) FROM {{ this }})
{% endif %}

• Selective Columns: Instead of selecting all columns (SELECT *), explicitly select only the columns you need. This reduces the data load and speeds up the query.

3. Use Indexes (if supported by your database)

Some data warehouses support indexing, which can improve query performance by allowing faster lookups on specific columns. If your data warehouse allows it, consider indexing columns that are commonly used for filtering or joining, such as id or updated_at.

4. Manage Memory and Resources Effectively

Depending on the complexity of your queries, dbt incremental models can consume significant resources. Here are a few ways to manage memory and resources effectively:

• Break Down Complex Logic: If your incremental model involves complex transformations, consider breaking it down into multiple models or Common Table Expressions (CTEs). This can reduce memory load and make it easier to debug.
• Use Temporary Tables: For complex transformations or intermediate steps, use temporary tables to stage data, reducing the amount of data loaded into memory at once.

5. Avoid Unnecessary Full Refreshes

While full refreshes can be useful for resetting your data, they are resource-intensive and should be avoided unless absolutely necessary. Schedule full refreshes sparingly and only when required to handle issues like schema changes or large deletions.

dbt run --full-refresh --select my_incremental_model

6. Monitor and Adjust Incremental Logic Over Time

As your data grows, the logic that worked initially may need adjustments. Monitor the performance of your incremental models and adjust your logic if you notice slowdowns or bottlenecks.

• Review Execution Logs: Check dbt’s execution logs regularly to see the number of records processed and any potential issues. This helps you identify areas for optimization.
• Adapt to Data Volume Changes: If your data volume increases significantly, consider revisiting your partitioning, clustering, or indexing strategy to handle the larger workload efficiently.

Conclusion

I hope this article has given you a solid understanding of incremental materialization in dbt and how it can make handling large datasets more efficient. By focusing only on new and updated data, incremental models help optimize both time and resources in your data pipelines.

If you found this article helpful, don’t forget to check out the previous articles in the Understanding dbt series, and stay tuned for more. Happy experimenting with incremental models, and as always, feel free to clap, leave comments, and share your own tips!

See you in the next article!