Why incremental models save time and cost in dbt - Why It Works This Way

Overview - Why incremental models save time and cost

What is it?

Incremental models in dbt are a way to update only new or changed data instead of rebuilding entire datasets every time. This means that when you run your data transformations, dbt processes just the new rows or updates, saving effort. It helps keep your data fresh without repeating work on data that hasn't changed. This approach is especially useful for large datasets where full rebuilds take a long time.

Why it matters

Without incremental models, every data update would require reprocessing all data from scratch, which wastes time and computing resources. This can slow down decision-making and increase costs for cloud storage and computing power. Incremental models make data workflows faster and cheaper, enabling businesses to get timely insights without overspending.

Where it fits

Before learning incremental models, you should understand basic dbt models and SQL transformations. After mastering incremental models, you can explore advanced dbt features like snapshots and testing. Incremental models fit into the data pipeline optimization stage, improving efficiency after you know how to build basic models.

Mental Model

Core Idea

Incremental models save time and cost by updating only new or changed data instead of rebuilding everything from scratch.

Think of it like...

It's like watering only the new plants in your garden instead of watering the entire garden every day. You save water and effort by focusing only where it's needed.

┌───────────────────────────────┐
│ Full Model Run                │
│ ┌───────────────┐             │
│ │ Process ALL   │             │
│ │ data rows    │             │
│ └───────────────┘             │
│                               │
│ Incremental Model Run          │
│ ┌───────────────┐             │
│ │ Process ONLY  │             │
│ │ new/changed   │             │
│ │ data rows    │             │
│ └───────────────┘             │
└───────────────────────────────┘

Build-Up - 6 Steps

1

Foundation

Understanding Full Data Processing

Concept: Learn how traditional data models rebuild entire datasets every time.

In a full data model, every time you run your dbt model, it processes all the data from the source tables. For example, if you have 1 million rows, dbt will reprocess all 1 million rows each time. This ensures data is fresh but can be slow and costly.

Result

Every run takes a long time and uses a lot of computing resources.

Understanding full data processing shows why it can be inefficient for large datasets.

2

Foundation

Basics of Incremental Models

Concept: Introduce the idea of processing only new or changed data rows.

Incremental models tell dbt to add or update only the rows that are new or have changed since the last run. This is done by defining a unique key and a filter condition to identify new data. Instead of rebuilding everything, dbt appends or updates just the necessary rows.

Result

Runs are faster because less data is processed.

Knowing that you can limit processing to new data is the key to saving time and cost.

3

Intermediate

Configuring Incremental Models in dbt

Concept: Learn how to set up incremental models using dbt configurations.

In your dbt model SQL file, you add a config block with 'materialized: incremental'. You also write a WHERE clause to filter new or updated rows, often using a timestamp or an ID. dbt uses this to decide which rows to process on each run.

Result

Your model runs incrementally, processing only relevant data.

Configuring incremental models correctly is essential to ensure only new data is processed.

4

Intermediate

Handling Updates and Deletes in Incremental Models

🤔Before reading on: Do you think incremental models automatically handle deleted rows? Commit to your answer.

Concept: Understand how incremental models deal with data changes beyond just new rows.

Incremental models can handle new and updated rows but do not automatically remove deleted rows from the source. To handle deletes, you may need additional logic or use dbt snapshots. This means incremental models are best for append or update scenarios.

Result

Incremental models keep data fresh for new and updated rows but may not reflect deletions without extra steps.

Knowing the limits of incremental models helps avoid data inconsistencies in production.

5

Advanced

Performance and Cost Benefits of Incremental Models

🤔Before reading on: Do you think incremental models always reduce cost? Commit to your answer.

Concept: Explore how incremental models reduce compute time and cloud costs in real scenarios.

By processing only new or changed data, incremental models reduce the amount of data scanned and transformed. This lowers cloud compute time and storage costs. For example, a model that takes 1 hour to run fully might take only 5 minutes incrementally, saving money and enabling faster insights.

Result

Significant reduction in runtime and cloud costs, enabling more frequent data updates.

Understanding cost savings motivates using incremental models in large-scale data projects.

6

Expert

Pitfalls and Advanced Strategies in Incremental Models

🤔Before reading on: Can incremental models cause data duplication if misconfigured? Commit to your answer.

Concept: Learn about common mistakes and advanced techniques to ensure data correctness with incremental models.

If the unique key or filter condition is incorrect, incremental models can duplicate or miss data. Advanced users implement checks, use dbt tests, and combine incremental models with snapshots or merge strategies to maintain data integrity. Understanding transaction isolation and concurrency is also important in production.

Result

Robust incremental models that maintain accurate and consistent data over time.

Knowing advanced pitfalls and fixes prevents costly data errors in production environments.

Under the Hood

Incremental models work by comparing the existing target table with the source data using a unique key and filter condition. dbt runs a SQL query that selects only new or changed rows based on this filter. It then inserts or updates these rows into the target table, leaving unchanged rows untouched. This reduces the amount of data processed and written.

Why designed this way?

Incremental models were designed to optimize data workflows by avoiding full rebuilds, which are expensive and slow for large datasets. Early data tools processed everything every time, causing delays and high costs. Incremental processing balances freshness and efficiency by focusing only on changes, a concept borrowed from database replication and ETL best practices.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│ Source Table  │──────▶│ Filter New/   │──────▶│ Insert/Update │
│ (All Data)    │       │ Changed Rows  │       │ Target Table  │
└───────────────┘       └───────────────┘       └───────────────┘
         ▲                                              │
         │                                              │
         └───────────────────── Existing Data ─────────┘

Myth Busters - 3 Common Misconceptions

Quick: Do incremental models automatically handle deleted rows? Commit to yes or no.

Common Belief: Incremental models update all changes including deletions automatically.

Reality: Incremental models only add or update rows; they do not remove deleted rows unless extra logic is added.

Why it matters: Assuming deletes are handled can lead to stale or incorrect data, causing wrong business decisions.

Quick: Do incremental models always reduce runtime regardless of data size? Commit to yes or no.

Common Belief: Incremental models always make runs faster no matter the data volume.

Reality: If the new data volume is large or filter conditions are inefficient, incremental runs can still be slow.

Why it matters: Overestimating speed gains can cause poor planning and unexpected delays.

Quick: Can incremental models cause duplicate rows if misconfigured? Commit to yes or no.

Common Belief: Incremental models never cause duplicates because dbt manages keys automatically.

Reality: Incorrect unique keys or filters can cause duplicates or missing data.

Why it matters: Data duplication corrupts analytics and requires costly fixes.

Expert Zone

1

Incremental models rely heavily on the uniqueness and stability of the key column; changing keys mid-project can break data integrity.

2

Concurrency issues can arise if multiple incremental runs happen simultaneously without proper locking or transaction management.

3

Combining incremental models with dbt snapshots allows tracking of historical changes and handling deletes more robustly.

When NOT to use

Incremental models are not suitable when source data changes frequently with deletes or complex updates; in such cases, full refreshes or snapshot strategies are better.

Production Patterns

In production, teams schedule incremental runs frequently to keep data fresh while running full refreshes during off-peak hours. They also implement automated tests and monitoring to catch incremental failures early.

Connections

Database Change Data Capture (CDC)

Incremental models build on the same idea of processing only data changes.

Understanding CDC helps grasp how incremental models efficiently track and apply data updates.

Software Incremental Compilation

Both incremental models and incremental compilation avoid redoing work by focusing on changed parts.

Recognizing this pattern across fields shows how incremental approaches save time and resources broadly.

Lean Manufacturing

Incremental models reflect lean principles by eliminating wasteful full rebuilds.

Seeing incremental modeling as a lean process highlights its role in efficient resource use.

Common Pitfalls

#1 Not defining a unique key for incremental models.

Wrong approach:

config {
materialized: 'incremental'
}

select * from source_table

Correct approach:

config {
materialized: 'incremental',
unique_key: 'id'
}

select * from source_table where updated_at > (select max(updated_at) from target_table)

Root cause:

Without a unique key, dbt cannot identify which rows to update or insert, causing errors or duplicates.

#2 Using incremental models without a proper filter for new data.

Wrong approach:

config {
materialized: 'incremental',
unique_key: 'id'
}

select * from source_table

Correct approach:

config {
materialized: 'incremental',
unique_key: 'id'
}

select * from source_table where updated_at > (select max(updated_at) from target_table)

Root cause:

Without filtering, the model processes all data every time, negating incremental benefits.

#3 Assuming incremental models handle deletes automatically.

Wrong approach:

config {
materialized: 'incremental',
unique_key: 'id'
}

select * from source_table where updated_at > (select max(updated_at) from target_table)

Correct approach:

Use dbt snapshots or additional logic to track and remove deleted rows.

Root cause:

Incremental models only add or update rows; they do not detect deletions by default.

Key Takeaways

Incremental models process only new or changed data, saving time and cloud costs compared to full rebuilds.

Proper configuration with unique keys and filters is essential to ensure data accuracy and avoid duplicates.

Incremental models do not handle deletions automatically; additional strategies are needed for full data correctness.

Using incremental models enables faster data updates, supporting timely business decisions and efficient resource use.

Understanding the limits and pitfalls of incremental models helps build robust, production-ready data pipelines.