Materializations View Table Incremental

Why this matters

As an Analytics Engineer, you balance speed, cost, and data freshness. In dbt, choosing the right materialization (view, table, incremental) directly affects query performance, warehouse costs, and model run times.

• Speed up dashboards by caching heavy transformations as tables.
• Control compute costs by avoiding full rebuilds with incremental models.
• Keep data fresh where it matters (recent partitions) without reprocessing history.

Concept explained simply

Materialization tells dbt how to build and store your model in the warehouse:

• view: Stores only the SQL; every query recomputes the results. Lowest cost, slower queries for heavy logic.
• table: Stores the results as a physical table. Faster to query, more storage, must be rebuilt to update.
• incremental: Builds once, then only processes new or changed rows. Fast and cost-efficient for large, append-heavy tables.

How to choose the right materialization

Worked examples

Example 1: Lightweight transformation as a view

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

select
  currency_code,
  upper(currency_name) as currency_name,
  current_timestamp as refreshed_at
from {{ ref('stg_currency') }}

Use when the table is small or queries are infrequent. Warehouse recomputes on each query, but cost remains low.

Example 2: Precomputed table for a dashboard

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

with orders as (
  select * from {{ ref('stg_orders') }}
),
items as (
  select * from {{ ref('stg_order_items') }}
)
select
  o.order_date::date as order_date,
  sum(items.quantity * items.unit_price) as revenue
from orders o
join items on items.order_id = o.order_id
group by 1

Good for heavy aggregations that power dashboards. Rebuilt on each run; fast to query later.

Example 3: Incremental model with merge strategy

{{ config(
  materialized='incremental',
  unique_key='event_id',
  incremental_strategy='merge'
) }}

select
  event_id,
  user_id,
  event_type,
  event_timestamp
from {{ ref('stg_events') }}
{% if is_incremental() %}
  where event_timestamp > (select coalesce(max(event_timestamp), '1900-01-01') from {{ this }})
{% endif %}

On the first run, the full dataset loads. On subsequent runs, only newer rows are selected and merged by unique_key.

{{ config(
  materialized='incremental',
  unique_key='event_id',
  incremental_strategy='merge',
  partition_by={'field': 'event_date', 'data_type': 'date'},
  cluster_by=['event_type']
) }}

Exercises

These mirror the exercises below so you can practice and then check solutions.

Exercise 1: Pick and configure the right materialization

You have a small dimension table (500 rows) that changes rarely (monthly). Convert the model to a view or table and justify your choice. Add the config block and final SQL.

• Confirm the materialization choice.
• Add a config block at the top.
• Ensure the SQL compiles without warehouse-specific features.

Exercise 2: Make a safe incremental model

Convert a daily appended orders feed into an incremental model that only processes new rows based on order_created_at and merges by order_id.

• Use unique_key and incremental_strategy='merge'.
• Use is_incremental() to filter.
• Ensure backfill works via full-refresh when needed.

Common mistakes and self-check

• Forgetting unique_key in incremental models. Self-check: Do you have a stable unique identifier? If not, reconsider incremental.
• No is_incremental() filter. Self-check: Does your SQL select all history on every run? Add a where clause wrapped in is_incremental().
• Using view for heavy logic. Self-check: Are dashboards slow? Consider switching to table.
• Ignoring late-arriving data. Self-check: Do you have late updates? Consider filtering by a moving window (e.g., last 7 days) or using a merge with unique_key.
• Schema drift breaks runs. Self-check: If source adds columns, set on_schema_change to append_new_columns or handle explicitly.
• Not planning for deletes. Self-check: If source deletes rows, do you need soft-deletes, tombstones, or periodic full-refresh?

Practical projects

• Project 1: Rebuild your top 3 dashboard models. Choose view/table/incremental for each, measure run time and query speed before vs after.
• Project 2: Implement an incremental fact model with a 7-day rolling reprocess window to handle late-arriving data.
• Project 3: Add partitioning/clustering to a large incremental model, document the config, and compare cost and performance.

Mini challenge

Choose materializations for these cases and explain why:

• Case A: 300-row country lookup used in joins.
• Case B: 200M-row clickstream with daily appends; dashboards read last 30 days.
• Case C: Monthly financial snapshot rebuilt from multiple systems.

Quick Test

This short test is available to everyone. If you’re logged in, your progress will be saved.

Who this is for

• Analytics Engineers and Data Analysts using or adopting dbt.
• Data Engineers optimizing warehouse cost and performance.
• BI Developers preparing fast, reliable datasets for dashboards.

Prerequisites

• Comfort with SQL selects, joins, and aggregations.
• Basic understanding of dbt models and refs.
• Access to a data warehouse (conceptually sufficient for learning here).

Learning path

1. Review dbt model basics and configuration blocks.
2. Practice with view and table materializations.
3. Implement incremental models with unique_key and is_incremental().
4. Add partitioning/clustering where helpful.
5. Benchmark and document performance improvements.

Next steps

• Finish the exercises and take the Quick Test below.
• Refactor one real model in your environment to the optimal materialization.
• Share results with your team: run time, query latency, and cost impact.