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Deploying to Dev

You can run any dbt command in the terminal in RStudio (JupyterLab coming soon) to deploy models and seeds, or to run tests. When you deploy models and seeds from RStudio the database they are built in to will be suffixed with _dev_dbt and the underlying data that gets generated will be written to the following S3 path:


The data in S3 and the Glue catalog entry for dev databases and tables is deleted approximately every 10 days. If you come back after a break and find your tables are missing, just rerun dbt.

When you’re ready, raise a pull request to merge your changes into the main branch. When you do this a number of workflows will run including, deploying your changes to dev, and SQL and YAML linting checks. Run artefacts for the dev deployment are exported to S3 and are available for 3 days. To get the S3 path that your run artefacts have been exported to, check the Export run artefacts to S3 output of the Deploy dbt dev workflow. The path will look something like:


Helpful commands

As previously mentioned, the mojap_derived_tables dbt project is a shared space and so when deploying your models and other resources you’ll want to make use of the node selection syntax so you don’t end up running everyone else’s. Don’t worry if you do, it will just take a long time to run and then the deployed resources will eventually be deleted. You should cancel the execution with ctrl+c or ctrl+z and save yourself some time.

To select a single model or seed, add the following to your dbt command:

--select database_name_model_name

or a number of models by running:

--select database_namemodel_1_name database_name_model_2_name

To select a full directory of models, add the following to your dbt command:

--select models/.../path/to/my/models/

As you develop and run your models you’ll generate a lot of logs and run artefacts. This can become unwieldy and messy so it’s helpful to clear them out from time to time. To remove run artefacts from previous invocations of dbt, run:

dbt clean

To check your SQL and YAML is syntactically correct, run:

dbt compile --select models/.../path/to/my/models/

To deploy your models, run:

dbt run --select models/.../path/to/my/models/

To deploy your seeds, run:

dbt seed --select seeds/.../path/to/my/seeds/

Don’t forget that if your models depend on your seeds, you’ll need to deploy your seeds before your models.

To run tests on models with tests defined, run:

dbt test --select models/.../path/to/my/models/

Using the + prefix

The + prefix is a dbt syntax feature which helps disambiguate between resource paths and configurations in the dbt_project.yml file. If you see it used in the dbt_project.yml file and wonder what it is, read dbt’s guidance on using the + prefix. It is also used to configure properties in a nested dictionary which take a dictionary of values in a model, seed or test config .yaml. For example, use +column_types rather than column_types since what follows are further key and value pairs defining the column names and the required data type. It doesn’t hurt to use + prefix so it is recommended to always do so.

version: 2

models: - name: prison_safety_and_security_question_answer_fct description: The question and answer fact table. config: materialized: incremental incremental_strategy: append partitioned_by: ['snapshot_date'] +column_types: column_1: varchar(5) column_2: int column_3: string

How to use the incremental materialisation with the append strategy

You may want your final derived table to retain previous versions of itself and not be overwritten each time your table is deployed. The following example will detail how you can achieve creating snapshots of the data and partitioning the table by those snapshots.

If you had a model producing a final table at models/some_domain/some_database/some_databasefinal_derived_table.sql with a snapshot date column of table_snapshot_date you should have a respective YAML config file saved at models/some_domain/some_database/some_database_final_derived_table.yml looking something like the below (with the names of your model and columns)

version: 2

models: - name: some_database__final_derived_table

config: # makes the model append any new data to the existing table partitioned # by the list of columns given. Effectively creating snapshots materialized: incremental incremental_strategy: append partitioned_by: ['table_snapshot_date'] tags: monthly

description: what a table.

columns: - name: unique_id description: a unqiue identifier - name: something_interesting description: some good data - name: table_snapshot_date description: snapshot date column and also table partition.

One important thing to note is that partition columns need to be the last column in your table. And if you have multiple partition columns they would all need to be the last columns and in the same order in the paritioned_by key list value in your yaml config as they appear in your table.

If you have defined the config for your model as above, every time you run dbt run --select ... locally via a command in your terminal, the data from that run will be appended to the previous data in the dev version of your database.

If you wanted to test the data is being materialised as intended then run once with an early snapshot date and again with a later snapshot date and inspect your data in athena, with a query like:

select table_snapshot_date, count(*)
from some_database_dev_dbt.final_dervied_table
group by table_snapshot_date

You can also inspect the s3 bucket and folder where your data will be saved. In the case of this example it would be mojap_derived_tables/dev/models/domain_name=some_domain/database_name=some_database/table_name=final_derived_table/. You’d expect to see a number of timestamped folders each containing a partition of your table’s data (based on how many times you’ve run your models).

If you want to run your models and disregard all previous snapshots you should add the flag --full-refresh to dbt run, e.g. dbt run --select models/some_domain/some_database/some_database__final_dervied_table.sql --full-refresh.

This page was last reviewed on 7 August 2023. It needs to be reviewed again on 7 August 2024 by the page owner #ask-data-modelling .
This page was set to be reviewed before 7 August 2024 by the page owner #ask-data-modelling. This might mean the content is out of date.