As data volumes increase, efficiently processing and transforming large datasets becomes crucial for analytics engineers. One effective way to optimize performance is by implementing incremental models—an approach where only new or modified data is processed instead of reprocessing the entire dataset. This technique is especially useful in big data environments like event streaming, where new data is continuously ingested.

In this guide, we will explore how analytics engineers can implement incremental models in dbt (data build tool), covering the various types of incremental models, best practices, and real-world examples. We will also discuss which big data systems benefit most from incremental strategies and provide insights into expected performance improvements.

Table of Contents

1. What is dbt?
2. What Are Incremental Models?
3. Types of Incremental Models
   • a) Date-based Incremental Models
   • b) ID-based Incremental Models
   • c) “Not In” Queries for Incremental Loading
4. How to Implement Incremental Models with dbt
5. Real-World Example: Event Data Processing
6. Which Big Data Systems Benefit Most from Incremental Models?
7. Performance Estimates: Incremental vs. Full Loads
8. Monitoring and Testing Incremental Models
9. Common Pitfalls and How to Avoid Them
10. Conclusion

1. What is dbt?

dbt (data build tool) is an open-source tool used by data analysts and engineers to transform raw data in a warehouse into analytics-ready datasets. It allows users to define SQL transformations as code, run tests to ensure data quality, and manage dependencies between models.

One of the key features of dbt is its support for incremental models, which enable efficient data processing by transforming only new or modified data.

2. What Are Incremental Models?

An incremental model in dbt is a way to process only new or modified records in a dataset, rather than reprocessing the entire dataset. Incremental models reduce processing time, lower costs (especially in cloud data warehouses), and improve the scalability of data pipelines.

For example, in an event tracking system, new events are continuously ingested. Reprocessing the entire event dataset every time would be inefficient. Instead, an incremental model would process only the new events since the last update, optimizing both time and resources.

3. Types of Incremental Models

There are several ways to implement incremental models based on the structure of your data and business requirements. Below are the three most common types of incremental models used by analytics engineers.

a) Date-based Incremental Models

Date-based incremental models are the most common type of incremental model. They filter records based on a timestamp or date column, processing only rows that have been added or updated since the last run. This strategy works well for systems that store transactional data, such as logs or sales data, with clear timestamps indicating when records are created or modified.

Example: Processing data with a timestamp

-- models/event_incremental.sql
{{ config(
    materialized = 'incremental',
    unique_key = 'event_id'
) }}

with new_events as (
    select
        event_id,
        user_id,
        event_time,
        event_type
    from
        raw.events
    where
        event_time > (select max(event_time) from {{ this }})
)

select
    *
from new_events

In this example:

• We are processing events that occurred after the latest event_time in the existing dataset.
• The unique_key is the event_id, which ensures that only unique events are added.

b) ID-based Incremental Models

ID-based incremental models are useful when you have a sequential or unique identifier for each record (e.g., an auto-incrementing order_id). Instead of using a date, these models filter records based on their ID.

Example: Processing data with an incrementing ID

-- models/transactions_incremental.sql
{{ config(
    materialized = 'incremental',
    unique_key = 'transaction_id'
) }}

with new_transactions as (
    select
        transaction_id,
        user_id,
        transaction_date,
        amount
    from
        raw.transactions
    where
        transaction_id > (select max(transaction_id) from {{ this }})
)

select
    *
from new_transactions

Here:

• We are processing only the records with transaction_id greater than the maximum transaction_id in the current model.
• This works well in systems where records are appended in a strict sequence, such as financial transactions or customer orders.

c) “Not In” Queries for Incremental Loading

Sometimes, data doesn’t follow a strict sequence or timestamp, and you may need to compare datasets to identify new records. In this case, you can use a “not in” query to load records that are not already in the target table.

Example: Loading data with a “not in” query

-- models/users_incremental.sql
{{ config(
    materialized = 'incremental',
    unique_key = 'user_id'
) }}

with new_users as (
    select
        user_id,
        email,
        signup_date
    from
        raw.users
    where
        user_id not in (select user_id from {{ this }})
)

select
    *
from new_users

In this case:

• We are loading new users who don’t already exist in the target table.
• This type of query is helpful when data is inserted without a consistent order, such as customer signups or user-generated content.

4. How to Implement Incremental Models with dbt

Now that we’ve covered the various types of incremental models, let’s dive into the implementation details.

Configuring an Incremental Model

To create an incremental model in dbt, use the config block to specify that the model should be materialized as incremental. This block is added to the SQL file for your model.

Here’s a basic template for configuring an incremental model:

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

with new_data as (
    select
        id_column,
        other_columns
    from
        raw.table
    where
        some_filtering_condition
)

select
    *
from new_data

In the config block:

• materialized = 'incremental' tells dbt to materialize the model incrementally.
• The unique_key specifies the column that uniquely identifies each row, ensuring only new or updated rows are processed.

For each type of incremental model (date-based, ID-based, or “not in”), the where clause will change based on how you determine what data is new.

5. Real-World Example: Event Data Processing

Now, let’s walk through a real-world example that focuses on processing event data. Many companies handle massive amounts of event data from user interactions, IoT devices, or system logs. Continuously ingesting and transforming this data in a scalable manner is crucial for timely insights.

Scenario

Imagine you are tracking user events on a web application, and the data is being logged into a raw table called raw.events. You want to create an incremental model that processes only new events and stores them in an event_incremental table for downstream analytics.

Here’s the structure of your raw.events table:


CREATE TABLE raw.events (
    event_id STRING,
    user_id STRING,
    event_time TIMESTAMP,
    event_type STRING
);

You want to build a dbt model that processes only new events, appending them to your existing event_incremental table.

SQL for Incremental Event Processing

-- models/event_incremental.sql
{{ config(
    materialized = 'incremental',
    unique_key = 'event_id'
) }}

with new_events as (
    select
        event_id,
        user_id,
        event_time,
        event_type
    from
        raw.events
    where
        event_time > (select max(event_time) from {{ this }})
)

select
    *
from new_events

In this example:

• We are processing only events that occurred after the latest event_time in the event_incremental table.
• This model will be much faster than reprocessing all the events, as it only loads new data.

Run the Incremental Model

To run the incremental model, use the following command:

dbt run --models event_incremental

This command processes only the new events, appending them to the existing data in event_incremental.

6. Which Big Data Systems Benefit Most from Incremental Models?

Incremental models are particularly useful in big data systems that handle high volumes of data and need to balance performance and cost. Some systems that benefit the most from incremental processing include:

• Event Tracking Systems: Systems that log user interactions or IoT events generate large volumes of data. Incremental models can be used to process only the latest events, reducing both time and costs in data warehouses like BigQuery, Snowflake, or Redshift.
• Transactional Systems: E-commerce or financial platforms dealing with transaction records can benefit from ID-based incremental models. Processing only new transactions ensures that data pipelines remain scalable as transaction volumes grow.
• Clickstream Data: Web analytics tools that track user behavior across websites generate massive clickstream data. Incremental models ensure that only new click events are processed, making real-time analytics feasible.
• Log Aggregation Systems: Logs from servers, applications, and security systems grow rapidly. Incremental models allow logs to be processed in chunks, making it possible to build scalable log aggregation pipelines.

7. Performance Estimates: Incremental vs. Full Loads

The performance improvements from incremental models can be significant, especially in cloud-based data warehouses that charge based on the amount of data processed.

Performance Comparison

1. Full Load Scenario: Let’s assume a full load processes 1 TB of data, which takes 2 hours to complete on a cloud data warehouse like BigQuery. The cost is proportional to the amount of data processed.
   • Time: 2 hours
   • Data Processed: 1 TB
2. Incremental Load Scenario: If you switch to an incremental model that processes only new records (e.g., 100 GB of new data), the process time could reduce drastically.
   • Time: 15-20 minutes
   • Data Processed: 100 GB

The performance gains will vary based on the data volume, warehouse technology, and the complexity of transformations, but in general, incremental models can reduce run times by 70-90% in high-volume systems.

8. Monitoring and Testing Incremental Models

To ensure your incremental models are working correctly, you should:

• Monitor Performance: Use your data warehouse’s query performance monitoring tools (e.g., BigQuery’s performance insights) to track how long incremental runs take and how much data is processed.
• Test Data Integrity: dbt’s built-in testing framework lets you define tests to ensure data quality. For example, you can check for duplicates or missing data after incremental loads.

Example of a simple dbt test:

version: 2

models:
  - name: event_incremental
    tests:
      - unique: event_id
      - not_null: event_id

9. Common Pitfalls and How to Avoid Them

Pitfall 1: Misconfigured Incremental Logic

One common issue is incorrectly configuring the filter logic (e.g., using max(event_time)). This could result in either missing new records or reprocessing the entire dataset.

Solution: Test your incremental logic with small datasets before applying it to production.

Pitfall 2: Handling Deletions or Updates

Incremental models are great for adding new records, but they don’t automatically handle deletions or updates. You’ll need to build additional logic for these scenarios.

Solution: For datasets with frequent updates or deletions, consider adding an update or delete process to your pipeline.

10. Conclusion

Incremental models in dbt provide analytics engineers with a powerful tool for building scalable data pipelines. By processing only new or modified data, incremental models drastically improve performance, reduce costs, and ensure scalability as data volumes grow.

In this guide, we explored:

• The types of incremental models (date-based, ID-based, “not in” queries).
• How to implement incremental models in dbt.
• A real-world example of processing event data incrementally.
• Performance benefits and how to monitor incremental models.

By adopting incremental models, you can ensure that your data pipelines remain efficient, even as your data grows.