Watch the video¶
Prefer a video? This video has a great overview of the content on this page.
- The AutomateDV package assumes you've already loaded a table with raw data from a source system or feed; this is referred to as the 'raw staging layer' or 'landing zone'.
The raw staging table needs to be prepared with additional columns so that we may load our raw vault. Specifically, we need to add hash keys, hashdiffs, and any implied fixed-value columns (see the above diagram).
We also need to ensure column names align with target Hub or Link table column names.
Creating staging models¶
To create a stage model, we simply copy and paste the below template into a model named after the staging table/view we are creating. AutomateDV will generate a stage using parameters provided in the next steps.
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The recommended materialisation for a stage is
view, as the stage layer contains minimal transformations on the
raw staging layer which need to remain in sync. You may materialise some or all stages as tables if necessary, though this
can increase costs significantly for large amounts of data.
Adding the metadata¶
Let's look at the metadata we need to provide to the stage macro.
The "source model" for a stage does not necessarily need to be a model. This means you do not need to manage the raw stage via dbt models if you don't need to, and can simply reference tables in your database via a dbt source.
The model provided in the 'Final model' section below, shows the use of the 'source style' source_model syntax.
What is the '!'?
This is some syntactic sugar provided by AutomateDV to create constant values. Read More
columns: CUSTOMER_NAME, CUSTOMER_ID, CUSTOMER_PHONE, CUSTOMER_DOB
When we provide the metadata above, our model should look like the following:
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See our metadata reference for more detail on how to provide metadata to stages.
In summary this model will:
- Be materialized as a view
- Select all columns from the external data source
- Generate hashed columns to create hash keys and a hashdiff
- Generate a
SOURCEcolumn with the constant value
- Generate an
EFFECTIVE_FROMcolumn derived from the
BOOKING_DATEcolumn present in the raw data.
END_DATEcolumns for use in the effectivity satellites later on.
Using the staging macro
Take a look at the stage section of the macro documentation for a more in-depth look at what you can do with the stage macro
With our model complete and our YAML written, we can run dbt:
dbt run -s v_stg_orders
The resulting stage view will look like this:
|CUSTOMER_HK||NATION_HK||CUSTOMER_NATION_HK||CUSTOMER_HASHDIFF||(source table columns)||LOAD_DATETIME||SOURCE||EFFECTIVE_FROM||START_DATE||END_DATE|
Now that we have implemented a new staging layer with all the required fields and hashes, we can start loading our vault.