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Create a Derived Table Instructions

⚠️ This service is in beta ⚠️

Read on to find out more or get in touch at the #ask-data-modelling channel.

Table of Contents

⚠️ This service is in beta ⚠️ Read on to find out more or get in touch at the #ask-data-modelling channel.

What is Create a Derived Table?

Create a Derived Table is a service that brings dbt, Git, and data access controls together to allow you to deploy tables derived from data available on the Analytical Platform; straight to Athena, in a reproducible way, and with scheduled table updates. All you’ll need to do is submit the SQL to derive your tables along with a few configuration files in a GitHub PR. dbt is at the core of Create a Derived Table and it’s packed full of features for transforming data using SQL so you’ll need to get familiar with certain bits of dbt syntax. To learn more about dbt, take a look at their documentation. Some of the basics about working with dbt are covered below, but you can also sign up and work through dbt’s own training courses for free. The training uses their own user interface instead of Create a Derived Table but it’s still relevant here.

We’re still in beta so we’d love to get some of you using Create a Derived Table to get your feedback to help guide data modelling best practice in the Ministry of Justice and make sure we can continue to improve the user experience.


The dbt project

This service should be used for creating tables with well defined use cases, like to serve a performance metric, publication, or MI report. This is because the dbt project (mojap_derived_tables) is a shared space and so knowing where to put your work in the broader structure of the project is key. That’s not to say you can’t explore and experiment with dbt within the dbt project, there’s a development envionment where you can try things out without having to worry about making a mess of things. More on that later.

Domains

The primary consideration relating to project structure is understanding which domain the table you want to create belongs to. In Create a Derived Table a domain should correspond to some service area or core business concept and is used to logically group databases. Domains are not mutually exclusive so the same concepts can exist in different domains. A domain may be ‘people’ relating HR and corporate, or ‘risk’ relating to a justice system service user’s safety, but it could be more or less granular if appropriate.

Databases

The secondary consideration is whether the tables you are creating belong in an existing database, if they do, then this step is easy. If you need to create a new database then you’ll need to decide which domain to put it in. It’s also possible to define a database across multiple domains. For example, a number of tables within your database might sit within ‘domain a’ while the rest sit in ‘domain b’. This approach has the benefit of keeping all tables logically grouped within one database but will result in access to those tables being limited by the domain.

Standard directory structure and naming conventions

The following is an example of how a team might build a data model whilst adhearing to the standard dbt project directory structure required to work with Create a Derived Table. The Prison Safety and Security team have created a database called prison_safety_and_security in the security domain.

  • From the mojap_derived_tables dbt project, the hierarchy of directories must follow models -> domain -> database. The directory structure after this is arbitrary and can be chosen to suit your needs. However, we do recommend that you arrange your models into data marts and suffix your models with descriptions (this will be discussed in more detail).
  • Directory and file names should only use snake case, as in, no-hyphens-like-this, just_underscores_like_this.
  • Models (.sql files) must be named by the database and table they relate to separated by double underscores, i.e., <database_name>__<table_name>.sql. This is because all models in the models directory must have a unique name.
  • Suffixes should be added that describe each model’s purpose.
    • Fact and dimension models should be suffixed with _fct and _dim respectively.
    • Intermediate models should be suffixed with the main transformation being applied in past tense, e.g., _grouped or _left_joined.
  • You may want to arrange staging models into a specific staging database, or within a single database for all your tables.
    • Staging models should be suffixed with _stg unless built into a specific staging database.
  • Fact, dimension, and staging models must have their own property file that has the same filename as the model they define properties for.
  • Intermediate models should have a single configuration file named properties.yaml.
  • Seed property files must have the same filename as the seed they define properties for.

If you have ideas about how you would like to structure your data model, please get in touch as we’d love to hear from you to help guide best practice principles.

├── mojap_derived_tables
  ├── dbt_project.yml
  └── models
      ├── sources
      │   ├── nomis.yaml
      │   ├── oasys_prod.yaml
      │   ├── delius_prod.yaml
      │   ├── xhibit_v1.yaml
      │   ...
      ├── security  # domain
      │   ├── prison_safety_and_security  # database
      │   │   ├── marts
      │   │   │   ├── intermediate
      │   │   │   │   ├── prison_safety_and_security_inc_ids_grouped.sql  # intermediate table
      │   │   │   │   ├── prison_safety_and_securityquestions_filtered.sql  # intermediate table
      │   │   │   │   ├── prison_safety_and_securityquestion_set_joined.sql  # intermediate table
      │   │   │   │   └── properties.yaml  # intermediate tables property file
      │   │   │   └── question_answers  # arbitrary grouping
      │   │   │       ├── prison_safety_and_securitydates_dim.sql  # dimension table
      │   │   │       ├── prison_safety_and_securitydates_dim.yaml  # table property file
      │   │   │       ├── prison_safety_and_securityincidents_fct.sql  # fact table
      │   │   │       ├── prison_safety_and_securityincidents_fct.yaml  # table property file
      │   │   │       ├── prison_safety_and_securityquestion_answer_fct.sql  # fact table
      │   │   │       └── prison_safety_and_securityquestion_answer_fct.yaml  # table property file
      │   │   └── staging
      │   │       ├── prison_safety_and_securitynomis_mod_stg.md  # markdown to be rendered in documentation
      │   │       ├── prison_safety_and_securitynomis_mod_stg.sql  # staging table
      │   │       └── prison_safety_and_security_nomis_mod_stg.yaml  # table property file

Data modelling

Data modelling is hard, so if the considerations about domains, databases, or data model structures aren’t clear, reach out to the data modelling team and we’ll do our best to help you out.


Getting started

Standard database access

The first thing you’ll need to work with Create a Derived Table is an Analytical Platform account with standard database access. If you don’t have that already, follow the guidance on how to make changes to the standard database access project file in the Data Engineering Database Access repo.

Your Data Engineering Database Access project access file

As well as standard datbase access, you’ll need a project access file that’s specific to your (or your team’s) work. This will give you access to the source databases used to derive tables as well as the derived tables themselves. Access to derived tables is granted at the domain level, this means you will need to know which domain you will be creating derived tables in before you start working. Your project file should include the source databases and domain(s) that you’ll use to derive your tables from, and the domain(s) that your tables will go in. A list of already available domains can be found in the database access resources for Create a Derived Table. If you don’t know already, you can learn how to set up a project access file by following the guidance on access to curated databases in the Data Engineering Database Access repo.

A typical project access file might look like:

project_id: Analytical Team 1 Derived Tables

review_date: 2022-10-20 approver_position: >=G7 of Analytical Team 1 Derived Tables approver_email: approver.name@justice.gov.uk alternative_approver_emails: - alternative.approver-name@justice.gov.uk

resources: - create_a_derived_table/domain_a - create_a_derived_table/domain_b - source_database_a/full - source_database_b/full

users: - alpha_user_name_a - alpha_user_name_b - alpha_user_name_c

business_case: > To create derived tables for Analytical Team 1.

Set up the RStudio working environment

You’ll need an interactive development environment (IDE) to interact with the repository and write your SQL and YAML code, and a Python virtual environment for dbt to run in. The following sections will show you how to set that up. It’s worth noting at this point that you’ll just be using RStudio as an IDE to interact with the repository (and git), write SQL and YAML code, and to run dbt commands from the terminal in a Python virtual environment. There is no R programming going on. We’re currently not planning to get Create a Derived Table up and running with JupyterLab, as the RStudio IDE is sufficient.

Clone the repository using the RStudio GUI

Select file > New Project... > Version Control > Git and paste the following into the text fields and then select which directory you would like to clone the repository to.

  • Repository URL: git@github.com:moj-analytical-services/create-a-derived-table.git
  • Project directory name: create-a-derived-table

New R Studio version control project

Clone the repository using the terminal

cd to the directory where you’d like to clone the repository to and then run the following command:

git clone git@github.com:moj-analytical-services/create-a-derived-table.git

Setting up a Python virtual environment

In the terminal, cd into the root of the repository (create-a-derived-table). You can check you’re in the correct directory by runnnig pwd. Once you’ve done that run:

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Set the following environment variable in your Bash profile:

echo "export DBT_PROFILES_DIR=../.dbt/" >> ~/.bash_profile

Then source your Bash profile by running:

source ~/.bash_profile

You’ll need to be in the dbt project to run dbt commands. This is the mojap_derived_tables directory:

cd mojap_derived_tables

Then to check an active connection, run:

dbt debug

If that’s successful, to install dbt packages, run:

dbt deps

Congratulations, you’re now ready to get dbt-ing!

Collaborating with Git

If you’re working on a data model as part of a team you can take inspiration from the Gitflow Workflow to effectively collaborate and develop your data model. The following is a recommendation, so feel free to use another approach if there’s one that suits your team better.

  • Start with a main branch for your team. Because there is already a main branch in the repository, we suggest you create a branch from the main branch and name it something like project-name-main or team-name-main.
  • Next Analyst 1 is working on table_a, while Analyst 2 is working on table_b.
    • Analyst 1 should create a branch off project-name-main called a1/model-a-development, prefixing the branch name with their initials and a slash a1/.
    • Analyst 2 should create a branch off project-name-main called a2/model-b-development, prefixing the branch name with their initials and a slash a2/.
  • When they have completed the development of their respective models, each analyst should raise a pull request for their respective branches and set the base branch to project-name-main.
  • The analysts, or someone else in the team, can then review the pull request and quality assure the work.
  • When a pull request has been merged into project-name-main, keep other feature branches like a2/model-b-development up to date by merging project-name-main into it. You can do this using the terminal or in the pull request itself where you will see a button at the bottom of the pull request page that says Update branch. If you do use the pull request to update the branch, you’ll need to run git pull locally. When the data model is complete and all changes have been merged into project-name-main, you can request review from the data modelling team who will check it over before it gets merged into main.

Creating branches

First make sure you’re on the main branch by running the following in terminal:

git status

If you’re not, run:

git checkout main
git pull

Next create your project-name-main branch (don’t forget to update the project name in the command) by running:

git checkout -b <project-name-main>

From the project-name-main branch, create your development branch by running:

git checkout -b <a1/model-a-development>

replacing a1 with your initals.

Updating your branch with main

When working on your models it is likely that your branch will get out of date with the main branch. To update you branch with the latest changes from main open a terminal and run the following:

Check your working tree, commit/push any changes if required

git status

Switch to the main branch and collect the latest changes, if any

git switch main
git fetch
git pull

Switch back to your branch and merge in the changes from main

git switch <your_branch>
git merge main -m "update branch with main"

At this point you may have merge conflicts that need to be resolved; please see GitHub resolve merge conflicts. If required, ask for help on the #ask-data-modelling slack channel.

Show indent guides in RStudio

In RStudio you can display vertical guidelines to help you keep track of indetations in your code; this is very helpful in YAML files. In the RStudio IDE go to

Tools → Global Options → Code → Display

and check the box for Show indent guides.

You will also want to set your tab width to 2 spaces, to do this go to

Tools → Global Options → Code → Editing

and check the box for Insert spaces for Tab and set Tab width to 2.


Creating models

What is a model?

A model is a select statement. Models are defined in .sql files and each .sql file contains one model/select statement. The term ‘model’ is almost synonymous with ‘table’ and for the most part can be used interchangeably and thought of the same thing. The term ‘model’ is used because a model can be materialised in different ways; it can be ephemeral, a view, or indeed a table. More on materialisations later. From here on the term ‘model’ will be used instead of ‘table’.

Model properties

Resources in your project — models, seeds, tests, and the rest — can have a number of declared properties. Resources can also define configurations, which are a special kind of property that bring extra abilities. What’s the distinction?

  • Properties are declared for resources one-by-one in .yaml files. Configs can be defined there, nested under a config property. They can also be set one-by-one via a config() macro directly in model (.sql) files, and for many resources at once in dbt_project.yml.
  • Because configs can be set in multiple places, they are also applied hierarchically. An individual resource might inherit or override configs set elsewhere.
  • A rule of thumb: properties declare things about your project resources; configs go the extra step of telling dbt how to build those resources in Athena. This is generally true, but not always, so it’s always good to check!

For example, you can use resource properties to:

  • Describe models, snapshots, seed files, and their columns
  • Assert “truths” about a model, in the form of tests, e.g. “this id column is unique”
  • Define pointers to existing tables that contain raw data, in the form of sources, and assert the expected “freshness” of this raw data
  • Define official downstream uses of your data models, in the form of exposures

Whereas you can use configurations to:

  • Change how a model will be materialised (table, view, incremental, etc)
  • Overwrite where model or seed data will be written to
  • Declare whether a resource should persist its descriptions as comments in the database
  • Apply tags and “meta” properties

Where can I define configs?

Configure a whole directory of models, seeds, tests, etc. from the dbt_project.yml file, under the corresponding resource key (models:, seeds:, tests:, etc). In the example below the materialized: table configuration has been applied to the entire mojap_derived_tables project. The sentences/ and question_answers/ directories have schedule tags configured for all models in those respective directories. ⚠️ Only add configurations to your own work! ⚠️

models:
  mojap_derived_tables:
    +materialized: table

prison:
  prison_safety_and_security:
    marts:
      sentences:
        +tags: monthly

      question_answers:
        +tags: weekly

Configure an individual model, seed, or test using a config property in a .yaml property file. This is the preferred method for applying configurations because it groups the configurations with defined properties for a given model, etc. and provides good visibility of what’s being applied. The below example applies the incremental materialisation and partitioned by configuration to the question_answer_fct model.

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

If for some reason it is not possible or reasonable to apply a configuration in a property file, you can use a config() Jinja macro within a model or test SQL file. The following example shows how the same configuration above can be applied in a model or test file.

{{
  config(
      materialized='incremental'
      incremental_strategy='append'
      partitioned_by=['snapshot_date']
  )
}}

Config inheritance

Configurations are prioritised in order of specificity, which is generally the inverse of the order above: an in-file config() block takes precedence over properties defied in a .yaml property file, which takes precedence over a configuration defined in the dbt_project.yml file. (Note that generic tests work a little differently when it comes to specificity. See dbt’s documentation on test configs.)

Writing models

Sources

Sources are descriptions of the databases and tables already in Analytical Platform. With those tables defined as sources in dbt, it is then possible to select from source tables in your models using the source() function which helps define the lineage of your data. To see which sources have been defined, look in the ./mojap_derived_tables/models/sources/ directory. If a database or table hasn’t been defined as a source, let the data modelling team know at #ask-data-modelling and we’ll look into adding it if it’s appropriate to do so. Below is an example of using the source() function to select from the contact table in the delius_prod database:

model_a.sql

select * from {{ source("delius_prod", "contact") }} limit 10

The ref function

The most important function in dbt is ref(); it’s impossible to build even moderately complex models without it. ref() is how you reference one model within another as typically models are built to be ‘stacked’ on top of one another. These references between models are then used to automatically build the dependency graph. Here is how this looks in practice:

model_b.sql

select contact_id from {{ ref("model_a") }} 
where mojap_current_record=true

Materialisations

Materialisations are strategies for persisting dbt models in a warehouse. There are four types of materializations built into dbt. They are:


What are seeds?

Seeds are lookup tables easily created from a .csv file. Put the .csv in the ./mojap_derived_tables/seeds/ directory and follow the same directory structure requirements and naming conventions as for models. As with marts models, your seeds should have property files that have the same filename as the seed. Seeds can be accessed by anyone with standard database access and so must not contain sensitive data. Generally, seeds shouldn’t contain more than 1000 rows, they don’t contain complex data types, and they don’t change very often. You can deploy a seed with more than 1000 rows, but it’s not reccomended and it will take quite a long time to build.

⚠️ Seeds must not contain sensitive data. ⚠️


Tests

Tests are assertions you make about your models and other resources (e.g. sources, seeds and snapshots). You can do things like test whether a column contains nulls or only unique values, compare row counts between tables, or check all of the records in a child table have a corresponding record in a parent table. dbt ships with some tests you can use but there many more out there in packages like dbt_utils, so do checkout dbt’s package hub for what’s available.

For more information on tests, see dbt’s tests documentation.

Custom generic tests

A test is really just a SQL query that returns rows that meet a certain criteria. If the number of returned rows is not zero, then the test fails. When you know that, you’ll soon realise that it’s not too difficult to write your own tests. Tests you write yourself are called custom generic tests and are writen in special macros; read the dbt guidance on how to write custom generic tests to find out more. There are some requirements specific to Create a Derived Table that you must follow when writing custom generic tests. They must live subdirectory named after the team, project, or database they relate to in the ./mojap_derived_tables/tests/generic/ directory and should follow a naming convention where the subdirectory and test name are separated by double underscores, for example team_name__test_name. This is so that ownership of the test is clear and so that the filename is unique.

├── mojap_derived_tables
  ├── dbt_project.yml
  └── tests
      └── generic
          ├── prison_safety_and_security  # team name
              ├── prison_safety_and_security_test_name.sql
              ...
          ├── other_project_name  # project name
              ├── other_project_nametest_name.sql
              ...
          ├── other_database_name  # database name
              ├── other_database_name_test_name.sql
              ...
          ...

Singular tests

Singular tests should not be used and they will not be run in the production environment.

Configuring tests

Defining tests for your resources is done within the property file for that resource under the tests property. See the example below.

version: 2

models: - name: <model_a> tests: - dbt_utils.equal_rowcount: compare_model: <model_b> columns: - name: <column_a> tests: - not_null - dbt_utils.not_constant


What else?

Macros

Macros are just Jinja functions. You can write your own macros which should live at ./mojap_derived_tables/macros/, see dbt’s docs on Jinja macros.

dbt packages

There is an ecosystem of packages containing helpful macros and tests you can use, see dbt package hub. If you need a package adding to the dbt project, update the packages.yml file then rerun dbt deps.

dbt artefacts

When dbt runs it generates artefacts. The most useful of these to you will be the logs in the ./mojap_derived_tables/logs/ directory and compiled SQL in the ./mojap_derived_tables/target/ directory. Compiled SQL can be useful when debugging errors as error messages will often refer to the line number in the compiled SQL and not that in your model file.

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

Linting

Linting is the automated checking of your code for programmatic and stylistic errors performed by running a ‘linter’. The linter will analyse your code and output error messages to the terminal. Linters should be run from the root of the repository, i.e., the create-a-derived-table directory.

Linting YAML files

YAML demands 2 spaces (not 1 tab) for each indentation, it can be frustrating, if you get spaces and tabs muddled (they are not always equivalent) as you can get a lint error but not be able to see it easily. We recommend setting up vertical guidlines in RStudio to keep track of your indentations, see above Show indent guides in RStudio, and to use the spacebar to create 2 spaces for indentation.

To lint a single YAML file, run:

yamllint .../path/to/yaml/file.yaml

Or a whole directory of YAML files by running:

yamllint .../path/to/yaml/directory/

Folded style >

Use > to split code over multiple lines; each newline is interpreted as a space (unless the newline is on an empty line or after a differently indented line) hence

  description: >
    the quick brown fox
    jumped over the lazy dog

is interpreted the same as

  description: the quick brown fox jumped over the lazy dog

Literal style |

Use | (pipe) to keep newlines, for instance when running subsequent commands in a GitHub action workflow

  run: |
    python -m pip install --upgrade pip
    pip install yamllint 

Linting and formatting SQL files

To lint a single SQL file, run:

sqlfluff lint .../path/to/sql/file.sql

Or a whole directory of SQL files by running:

sqlfluff lint .../path/to/sql/directory/

SQLFluff is a formatter as well as a linter. That means you can use it to edit your SQL files to match the linting rules. To format SQL files using SQLFluff replace lint in the commands above with fix.

.sqlfluffignore

The .sqlfluffignore file (found in the root of the repository) can be used to list files for SQLFluff to exclude from linting. This is only to be used if files continually fail linting, despite best efforts. To date this has only applied to complex macros. The default Jinja templater used by SQLFluff simply cannot deal with complex macros. The dbt templater should solve this issue, and we intend to trial it in the future.


Scheduling

There are three options for scheduling model updates: daily, weekly, and monthly. The monthly schedule runs on the first Sunday of every month and the weekly schedule runs every Sunday. All schedules run at 3AM. To select a schedule for your model, add the tags configuration to your model’s property file, like this:

version: 2

models: - name: <your_model_name> config: tags: daily

You can configure a directory of models to run on the same schedule by adding the tags configuration to the dbt_project.yml file by finding the models resource key and adding a few lines, like this:

models:
  mojap_derived_tables:  # this line is already in the file
    +materialized: table  # this line is already in the file
    security:
      prison_safety_and_security:
        +tags: daily
        staging:
          +tags: monthly

In the above example, the Prison Safety and Security team have configured their entire database to update daily except for the subdirectory of staging models that will update monthly.


Deploying your tables

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:

s3://mojap-derived-tables/dev/models/domain_name=domain/database_name=database_dev_dbt/table_name=table/data_file

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:

s3://mojap-derived-tables/dev/run_artefacts/run_time=2000-01-01T00:00:00/

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_namemodel_name

or a number of models by running:

--select database_namemodel_1_name database_namemodel_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/

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.

Prod

A model or seed is in ‘prod’ when they are merged into main. The data modelling team will review and approve any changes before a pull request is merged into main. If you have added or modified seeds in your pull request, these will be deployed when the pull request is merged. If you have added or modified models in your pull request, these will be deployed at the next schedule as per the defined schedule tag. Run artefacts for prod deployments are exported to S3 and are available for 3 days. You can get the S3 path by navigating to the Actions tab in the Create a Derived Table repository and selecting the workflow you want the output of. Then check the Export run artefacts to S3 output for the S3 path which will look something like this:

s3://mojap-derived-tables/prod/run_artefacts/run_time=yyyy-mm-dd hh:mm:ss/


Resources

  • Learn more about dbt in the docs
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License

Unless stated otherwise, the codebase is released under the MIT License. This covers both the codebase and any sample code in the documentation.

The documentation is © Crown copyright and available under the terms of the Open Government 3.0 licence.

This page was last reviewed on 15 September 2022. It needs to be reviewed again on 15 September 2023 by the page owner #ask-data-modelling .
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