dbt Ephemeral Models

In dbt (data build tool), ephemeral models are a powerful concept for creating reusable, temporary SQL transformations that do not generate tables or views in your data warehouse. Instead, they are inlined as Common Table Expressions (CTEs) into downstream models.

Ephemeral models allow you to modularize logic, improve readability, and keep your warehouse clean without creating unnecessary physical objects. They are especially useful for small, intermediate transformations used in multiple downstream models.

In this article, we will explore:

1. What ephemeral models are and how they work
2. Key characteristics and rules
3. Three detailed examples with SQL code
4. A Merquine-style diagram showing ephemeral model execution
5. Memory techniques for interviews and exams
6. Why ephemeral models are important
7. Common pitfalls and best practices

1. What Are Ephemeral Models?

Conceptual Definition

• An ephemeral model is a dbt model materialized as ephemeral.
• It does not create a physical table or view in the warehouse.
• When referenced via ref() in a downstream model, dbt inlines its SQL as a CTE in the consuming model.
• They are temporary: their logic exists only at compile time, not as a database object.

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

select
  user_id,
  lower(email) as email_clean
from {{ ref('raw_users') }}

If another model references this ephemeral model, dbt will insert the SQL above as a CTE in that model, rather than creating a separate table or view.

1.1 Key Characteristics

1.2 Why Use Ephemeral Models?

1. Reduce warehouse clutter: Avoid unnecessary tables and views.
2. Encourage modularity: Break complex SQL into smaller, reusable blocks.
3. Improve maintainability: Centralize transformations like cleaning, formatting, or filtering.
4. Optimize query compilation: Complex transformations can be nested directly into downstream logic without intermediate storage.

2. Ephemeral Models vs Other Materializations

Key difference: ephemeral models are never stored, only inlined, making them ideal for intermediate transformations.

3. How Ephemeral Models Work

1. Write the ephemeral model

   {{ config(materialized='ephemeral') }}
   select user_id, lower(email) as email_clean from {{ ref('raw_users') }}

2. Reference it in another model

   {{ config(materialized='table') }}
   with cleaned_users as (
       select * from {{ ref('ephemeral_clean_users') }}
   )
   select
       u.user_id,
       u.email_clean,
       o.order_id
   from cleaned_users u
   join {{ ref('raw_orders') }} o on u.user_id = o.customer_id

3. dbt compile: The SQL from the ephemeral model is inserted into the CTE cleaned_users.

4. dbt run: Downstream model executes the SQL, including inlined ephemeral logic.

Benefit: You never create an extra table, but downstream models can still reuse the logic.

4. Example Programs for Ephemeral Models

Below are three unique example scenarios showing ephemeral model usage.

Example Set 1: Cleaning User Data

ephemeral_clean_users.sql

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

select
    id as user_id,
    lower(trim(email)) as email_clean,
    coalesce(first_name, 'unknown') as first_name_clean,
    coalesce(last_name, 'unknown') as last_name_clean
from {{ ref('raw_users') }}
where active = true

downstream model using it:

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

with cleaned_users as (
    select * from {{ ref('ephemeral_clean_users') }}
)
select
    u.user_id,
    u.email_clean,
    count(o.order_id) as order_count
from cleaned_users u
join {{ ref('raw_orders') }} o on u.user_id = o.customer_id
group by 1, 2

Explanation:

• Cleaned users logic is reused but not stored as a separate table.
• Downstream table includes cleaned users directly in its CTE.

Example Set 2: Calculating Metrics for Reuse

ephemeral_user_metrics.sql

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

select
    user_id,
    sum(amount) as total_spent,
    count(distinct order_id) as orders_count
from {{ ref('raw_orders') }}
group by user_id

downstream model:

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

with metrics as (
    select * from {{ ref('ephemeral_user_metrics') }}
)
select
    u.id as user_id,
    u.signup_date,
    m.total_spent,
    m.orders_count
from {{ ref('raw_users') }} u
left join metrics m on u.id = m.user_id

Benefit:

• Aggregation logic is centralized and reused in any downstream model without creating intermediate tables.

Example Set 3: Filtering & Transforming Event Data

ephemeral_filtered_events.sql

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

select
    event_id,
    user_id,
    event_type,
    event_ts
from {{ ref('raw_events') }}
where event_type in ('purchase', 'signup')

downstream model:

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

with filtered_events as (
    select * from {{ ref('ephemeral_filtered_events') }}
)
select
    e.user_id,
    count(*) as event_count,
    max(event_ts) as last_event
from filtered_events e
group by e.user_id

Explanation:

• Filtering logic for specific event types is reusable in multiple analyses.
• No extra storage is used.

5. Merquine-Style Diagram for Ephemeral Models

raw tables
   │
   ▼
ephemeral models (inlined SQL)
   │
   ▼
downstream models (table/view/incremental)
   │
   ▼
BI dashboards / analytics

• Compile phase: dbt inlines ephemeral SQL into downstream models.
• Execution phase: warehouse executes the full query including inlined logic.
• No ephemeral table or view is created.

6. Memory & Interview/Exam Tips

Mnemonics

• “E-C-T-E” → Ephemeral = CTE = Temporary = Embedded
• Think: “Ephemeral never exists outside downstream”

Key Points to Remember

1. Ephemeral = inlined CTE
2. Cannot be queried directly
3. Reusable logic for downstream models
4. No storage, no incremental logic
5. Compile-time only

Flashcards / Q&A

• Q: Can you query an ephemeral model directly? A: No, it only exists inside downstream models.
• Q: How does dbt handle ephemeral models during dbt run? A: SQL is inlined into downstream CTEs; executed as part of downstream model.
• Q: Why use ephemeral instead of view? A: To avoid creating temporary warehouse objects and to keep logic modular.

Interview Talking Points

• Explain modularity & reusability.
• Explain performance benefit: no extra tables to maintain.
• Mention trade-offs: debugging can be harder; cannot isolate ephemeral model directly.

7. Importance of Ephemeral Models

1. Clean warehouse: Avoid unnecessary tables or views.
2. Reusable logic: Modularize SQL transformations, maintain a single source of truth.
3. Faster iterations: No separate build steps; ephemeral SQL executes inline.
4. Cost efficiency: No storage or materialization cost.
5. Encourages maintainable design: Simplifies dependency chains in large projects.

In modern analytics projects, ephemeral models are critical for intermediate transformations and modular pipelines.

8. Common Pitfalls & Best Practices

Best Practices

• Use ephemeral models for helper transformations, cleaning, or filtering
• Keep SQL small and readable
• Avoid nesting multiple ephemeral models deeply
• Use downstream tables/views for heavy computations
• Document ephemeral logic clearly in dbt docs

9. Summary

• Ephemeral models are temporary CTE-based models that do not create tables or views.
• They are inline-only, reusable, and ideal for modular transformations.
• Use them for cleaning, filtering, and aggregating small intermediate datasets.
• Cannot query directly, no incremental support, and are executed at compile-time.
• Ephemeral models improve maintainability, efficiency, and modularity in dbt projects.
• Use diagrams, examples, and mnemonics to remember the concept for interviews and exams.