Data Build Tool Summary¶

Dbt core is an open source CLI and database agnostic used to allow data analysts and engineers to build reliable, modular data pipelines, creating "models" (SELECT statements) that are version-controlled, automatically documented, and tested for quality before consumption by BI tools. Learn more about dbt in the docs.
dbt Cloud: A managed service with a web-based IDE, scheduler, job orchestration, and monitoring

Supported by ISVs in lake house market.

Use Cases¶

Modelling changes are easy to follow and revert
Explicit dependencies between models
Data quality tests
Incremental load of fact tables
Track history of dimension tables
Support automated testing, document generation, and data lineage visualization

Major Concepts¶

It encourages building complex transformations in smaller, reusable SQL steps, reducing repetitive code.
dbt uses a template mechanism (jinja), functions and a set of features to organize SQL and cross reference them.
The mandatory file for a project is the dbt_project.yml file as it contains information that tells dbt how to operate your project. dbt demarcates between a folder name and a configuration by using a + prefix before the configuration name.
Models: are the basic building blocks of the business logic. They includes materialized tables and views, and SQL files. Models can reference each others and use templates and macros.
Resources types includes models, seeds, snapshots, tests, sources
Properties describe resources
Configurations control how dbt builds resources in the warehouse. Could be set cross resources in dbt_project.yml, in a properties.yml under a folder, config() in a sql or resource file.

The dbt_project.yml may have many resources configured at once:

models:
  models:
    # namespace for model configs matching the project name
    airbnb:
      +materialized: view

The table below lists when to use View vs Table:

	View	Table
Purpose	Use for minor transformation	For intensive transformation
Execution	At runtime and when referenced	Pre-executed, with the results saved in tables
Storage	None	Need Storage space for materialized tables
Performance	Lot of steps leads to slower performance	Chained processes get improved perf.

dbt provides built-in testing (e.g., uniqueness, non-null checks) to catch broken logic
schema is the data contract of elements of the model, and define in a separate yaml file.
There are two macros to cross reference tables: {{ ref() }} use to reference a table within a model and {{source() }} to reference external data sources.

Install¶

Need Python, as dbt should be installed in a virtual environment. See installation instructions
Supported Python database
Create a $HOME/.dbt folder to let dbt persists the dbt-profile.yaml file to keep user and DB credentials.
Init a project: This command creates some folders to organize work inside the data project.
```
uv run dbt init --skip-profile-setup airbnb
```
See the examples in code/dbt/airbnb folder.
Or in virtual env created with uv and uv sync use:
```
dbt init
```
This will create a set of folders to manage all the needed elements of data pipelines.
```
models
analyses
tests
seeds
macros
snapshots
```
and the dbt_project.yml and profiles.yml files to define dbt settings.

pyproject.toml

The following dependencies are needed:

dependencies = [
    "dagster-dbt>=0.28.14",
    "dagster-webserver>=1.12.14",
    "dbt-autofix>=0.20.0",
    "dbt-core>=1.11.6",
    "dbt-snowflake>=1.8.4",
]

dbt_profile.yaml¶

profile.yaml defines the structure of the project, and keeps information to connect to database.
[dbt_profile]

Work on Models¶

Add Kimball structure as sources, dimensions, facts under the models folder
Add SQL materialized view using SELECT .... Do not use INSERT INTO, as it will be added

Validate each new SQL creation: within the folder with the dbt_profile.yaml, to build a view in Snowflake for example

dbt run

Example of output:

22:20:56  Found 1 model, 522 macros
22:20:56  
22:20:56  Concurrency: 1 threads (target='dev')
22:20:56  
22:20:57  1 of 1 START sql view model DEV.src_listings ................................... [RUN]
22:20:58  1 of 1 OK created sql view model DEV.src_listings .............................. [SUCCESS 1 in 1.17s]
22:20:59  
22:20:59  Finished running 1 view model in 0 hours 0 minutes and 2.78 seconds (2.78s).
22:20:59  
22:20:59  Completed successfully
22:20:59  
22:20:59  Done. PASS=1 WARN=0 ERROR=0 SKIP=0 NO-OP=0 TOTAL=1

and within Snowflake:

dbt run creates final sql queries under the target folder. This command can also apply to a specific table:
```
dbr run --select +models/facts/fct_reviews.sql
```

The + in front of the name specifies to deploy parents tables too.

Materializations¶

There are four materializations:

View: this is a lightweight representation of the data, not reused. no recreationg of the table as each execution.
Table: reusable data in external table- recreate at each run
Incremental: fact tables appends to tables - more like event data - table is not recreated each time.
Ephemeral (CTEs): aliasing of the data and filtering data. Not adversitized in the data warehouse. For example all the sql under the sources are becoming CTEs

Materialization can be set globally in the dbt_profile.yaml: all models are views, except in the dimensions folder where there are tables:

models:
  airbnb:
    +materialized: view
    dimensions:
      +materialized: table 
    sources:
      +materialized: ephemeral

Incremental¶

Specifying a fact table is incremental and add conditions for which the records are added to the table. The review_date of the record needs to be after the last record in the fct_reviews table:

{{
  config(
    materialized = 'incremental',
    on_schema_change='fail'
    )
}}
WITH src_reviews AS (
  SELECT * FROM {{ ref('src_reviews') }}
)
SELECT * FROM src_reviews
WHERE review_text is not null

{% if is_incremental() %}
  AND review_date > (select max(review_date) from {{ this }})
{% endif %}

Making a full-refresh:
```
dbt run --full-refresh
```

With the sources as ephemeral the output of dbt run becomes:

23:16:16  1 of 4 START sql table model DEV.dim_hosts_cleansed ............................ [RUN]
23:16:18  1 of 4 OK created sql table model DEV.dim_hosts_cleansed ....................... [SUCCESS 14111 in 1.93s]
23:16:18  2 of 4 START sql table model DEV.dim_listings_cleansed ......................... [RUN]
23:16:20  2 of 4 OK created sql table model DEV.dim_listings_cleansed .................... [SUCCESS 17499 in 2.47s]
23:16:20  3 of 4 START sql incremental model DEV.fct_reviews ............................. [RUN]
23:16:23  3 of 4 OK created sql incremental model DEV.fct_reviews ........................ [SUCCESS 0 in 2.37s]
23:16:23  4 of 4 START sql table model DEV.dim_listings_with_hosts ....................... [RUN]
23:16:24  4 of 4 OK created sql table model DEV.dim_listings_with_hosts .................. [SUCCESS 17499 in 1.58s]
23:16:24  
23:16:24  Finished running 1 incremental model, 3 table models in 0 hours 0 minutes and 9.83 seconds (9.83s).

dbt compile does not deploy to the target data warehouse

23:39:49  Running with dbt=1.11.6
23:39:50  Registered adapter: snowflake=1.11.2
23:39:50  Found 8 models, 1 seed, 3 sources, 522 macros
23:39:50  
23:39:50  Concurrency: 1 threads (target='dev')

Sources and Seeds¶

Seeds are local files that is uploaded to the data warehouse from dbt
Sources is an abstraction layer on top of the input tables. The source freshness can be checked automatically.

use dbt seed to populate the seed (csv file) to the data warehouse.

23:29:14  1 of 1 START seed file DEV.seed_full_moon_dates ................................ [RUN]
23:29:17  1 of 1 OK loaded seed file DEV.seed_full_moon_dates ............................ [INSERT 272 in 2.76s]

Sources may be defined in a yaml:

sources:
  - name: airbnb
    schema: raw
    tables:
      - name: listings
        identifier: raw_listings

      - name: hosts
        identifier: raw_hosts

      - name: reviews
        identifier: raw_reviews

From there the src_*.sql needs to be modified to do not reference ay table name in the data warehouse, but the source aliases.

WITH raw_hosts AS (
    SELECT
        *
    FROM
        {{ source('airbnb', 'hosts') }}
)

For source freshness, we need to consider one DATE column and add a config element to the table to define refreshness condition:

- name: reviews
  identifier: raw_reviews
  config:
    loaded_at_field: date
    freshness:
        warn_after: {count: 1, period: hour}
        error_after: {count: 24, period: hour}

Run the command: sbt source freshness to validate the data freshness.

Type-2 slowly changing dimensions¶

The goal is to keep history of changes to the records over time and not just the last record per key. dbt adds dbt_valid_from and dbt_valid_to columns to mark each records to be valid time from and to. A current correct records have dbt_valid_to sets to null.

snapshots live in the snapshot folder. There are two strategies for assessing data changes: * Timestamp: a unique key and updated_at fields is defined at the source model. These columns are used for determining changes * Check: any changes in a set of columns (or all columns) will be picked up as an update.

To create snapshots we need a yaml file under the snapshot folder:

snapshots:
- name: scd_raw_listings
    relation: source('airbnb', 'listings')
    config:
    unique_key: id
    strategy: timestamp
    updated_at: updated_at
    hard_deletes: invalidate

the dbt snapshot will create a new table with the columns added for the referenced table.

00:04:36  1 of 1 START snapshot DEV scd_raw_listings ..................................... [RUN] 
00:04:40  1 of 1 OK snapshotted DEV.scd_raw_listings ..................................... [SUCCESS 17499 in 3.44s]

* An update to an existing record and a new dbt snapshot will create historical record.

Tests¶

Two types of tests:
Unit Tests
Data Tests: run on actual data
There are two types of data tests: singular(SQL queries stored in tests) and generic
Defining test is by adding a schema.yaml with conditions on table. See example in models folder

To run the tests

dbt text --target duckdb -x
# -x it to continue even if one test fails

To debug a test, we can always look at where the dbt created the SQL to run, and execute this SQL in a SQL client, like duckdb cli.
By setting in the dbt_project.yaml
```
data_tests:
  _store_failures: true
```

Then any test failures will be saved in a new schema with table in the datawarehouse.

02:48:41  Failure in test accepted_values_dim_listings_cleansed_room_type__Private_room__Entire_home_apt__Shared_room__Hotelroom (models/schema.yaml)
02:48:41    Got 1 result, configured to fail if != 0
02:48:41  
02:48:41    compiled code at target/compiled/airbnb/models/schema.yaml/accepted_values_dim_listings_c_72f6cd1e8c350657dd7c7e44ed95fd70.sql
02:48:41  
02:48:41    See test failures:
---------------------------------------------------------------------------------------------------------------
select * from "airbnb"."main_dbt_test__audit"."accepted_values_dim_listings_c_72f6cd1e8c350657dd7c7e44ed95fd70"
---------------------------------------------------------------------------------------------------------------

See elementary-data.com
For unit test, we can define a yaml file at the same level as the SQL under validation. See unit_test.yaml, then run:
```
dbt test -s mart_fullmoon_reviews
```
Example to validate consistency among the created_at fields of the listings and reviews, we may want to add a singular test under the tests folder in the form of SQL:

Other Databases¶

Using dbt with DuckDB¶

Install dbt-duckdb python module: uv add dbt-duckd
Inport raw data to the Duckdb table or use Airflow to ETL such data. For example in code/dbt/airbnb/, bootstrap the DuckDB raw tables with:
```
export DBT_DUCKDB_PATH=./data/airbnb.duckdb
duckdb "${DBT_DUCKDB_PATH:-./data/airbnb.duckdb}" < scripts/bootstrap_duckdb_raw.sql
```

Run dbt against DuckDB:

cd airbnd
dbt seed --target duckdb
dbt run --target duckdb
dbt test --target duckdb

Use duckdb query engine to look at the tables: First duckdb ./data/airbnb.duckdb command. See the dot commands

.open data/airbnd.duckdb
.databases
.schema
.read FILENAME  Read and execute SQL from an external file
select * from raw.raw_hosts;

As duckDb can be embedded into Python code, it is possible to load a Pandas dataframe from a table in duckdb.

Using dbt with postgresql¶

Install Kubernetes Postgresql operator, then a postgres cluster and PGadmin webapp. See the minikube/posgresql folder

Do port forwarding for both Postgresql server and pgadmin webapp

kubectl port-forward service/pg-cluster 5432:5432
kubectl port-forward service/pgadmin-service 8080:80

get user, database name , password and URI from the postgresql secrets (see Makefile)
Create customers and orders tables, insert some records
Define the connection to the database in the .dbt/profiles.yaml
Validate with the connection dbt debug
Write some sql scripts in the models folder, then use dbt run and it will create new views in the default schema and one table. Example of join
The results can be also seen by querying the newly created views or tables.
```
select * from "default".customerorders;
```

Confluent Cloud Flink¶

Confluent dbt adapter aims to support standard dbt commands (init, debug, run, test, docs generate, etc.) against Confluent Cloud Flink, so teams can manage pipelines end-to-end from dbt rather than Terraform/REST only.

In Confluent Cloud Flink context, the dbt run does not process data; it deploys or updates the definition of a continuous dataflow to the streaming engine. User runs dbt run only when the SQL queries changes.

dbt concept mapping. A dbt schema is a Flink database, while a dbt database is a Flink Catalog, and finally a dbt identifier is a Flink Table. The schema field in profiles.yml actually refers to a Kafka cluster name. The database field refers to an environment ID. Some error messages reference "schema" when they mean "Kafka cluster/database".
The dbt mapping:

Dbt construct	CC Flink	dbt confluent
view	create view .. as select	view
table	snapshot query	streaming_table
incremental	not-supported
ephemeral	not supported
materialized_view	create table ... as select
seed	Faker /datagen connector	streaming_source

Flink Demos using dbt:
wd-flink-demo

Sources of Information¶

Udemy training from Zoltan C. Toth with Git Repo. Example of data from Inside AirBnB.
Dbt core
Preset
Snowflake username: jbcodeforce. Using key-pair authentication. Public key in Snowlflake
Youtube tutorial