Introduction

Data Vault is a data modeling technique that is specifically designed for use in Data Warehouses. It is a hybrid approach that combines the best elements of 3rd Normal Form (3NF) and Star Schema to provide a flexible and scalable data modeling solution.

A Data Vault consists of three main components: Hubs, Links, and Satellites.

Hubs are the backbone of the Data Vault architecture and represent the entities within the data model. They are the core data elements and contain the primary key information.

Links are used to establish relationships between hubs and provide the context for the data stored in the Satellites. They contain information about the relationships between hubs and are used to track changes in relationships over time.

Satellites contain all of the descriptive information about a particular hub. They provide additional details and attributes about the data stored in the hub and can contain information about different aspects of the data, such as historical data, attributes, and measurements.

Let’s look at an example of how to write a Hub, Link, and Satellite using Data Vault in Python and dbt.

Setup

First, we need to set up our environment and install the necessary packages. This example assumes that you have Python and dbt installed. If not, you can install them using the following commands:

pip install python 
pip install dbt

Next, we will create a new dbt project and initialize the environment:

dbt init

Now that we have our environment set up, let’s create our Hub, Link, and Satellite.

Hub:

{% macro hub(name, columns) %}
{{
  config({
    "materialized": "table",
    "unique_key": "{{ name }}_hash",
    "distribution": "even"
  })
}}

{{
  reference("{{ name }}_hub",
    columns=[
      {{ for column in columns -}}
        {{ column }},
      {{- end }}
    ],
    materialized="table",
    unique_key="{{ name }}_hash",
    distribution="even"
  )
}}
{% endmacro %}

Link:

{% macro link(name, left, right, columns) %}
{{
  config({
    "materialized": "table",
    "unique_key": "{{ name }}_hash",
    "distribution": "even"
  })
}}

{{
  reference("{{ name }}_link",
    left_key="{{ left }}_hash",
    right_key="{{ right }}_hash",
    columns=[
      {{ for column in columns -}}
        {{ column }},
      {{- end }}
    ],
    materialized="table",
    unique_key="{{ name }}_hash",
    distribution="even"
  )
}}
{% endmacro %}

Satellite:

{% macro satellite(name, hub, columns) %}
{{
  config({
    "materialized": "table",
    "unique_key": "{{ name }}_hash",
    "distribution": "even"
  })
}}

{{
  reference("{{ name }}_satellite",
    hub_key="{{ hub }}_hash",
    columns=[
      {{ for column in columns -}}
      {{ column }}, {{- end }} ], 
   materialized="table", 
   unique_key="{{ name }}_hash", 
   distribution="even"
  )
}}
{% endmacro %}

With these macros in place, we can now use them to define our Hub, Link, and Satellite tables. For example, let’s create a Hub for Customers:

Next, let’s create a Link between Customers and Orders:

{{ 
link("customer_order", "customer", "order", 
    ["order_id", "order_date"]
    ) 
}}

And finally, let’s create a Satellite for the Orders:

{{ 
    satellite("order", "order", 
            ["order_id", "product_id", "quantity", "total_price"]
            ) 
 }}

With these tables defined, we can now use dbt to build and populate our Data Warehouse.

Data Vault data modeling technique provides a flexible and scalable solution for managing data in Data Warehouses. By using hubs, links, and satellites, you can effectively manage relationships between entities, track changes over time, and store descriptive information about your data. By using Python and dbt, you can automate the process of building and maintaining your Data Warehouse, making it easier to keep your data up-to-date and accurate.

Finally, it’s important to note that while Data Vault is a powerful and flexible data modeling technique, it’s not the right solution for every scenario. Like all data modeling techniques, it has its own set of strengths and weaknesses, and it’s important to carefully consider your specific use case before deciding to use it.

That being said, for organizations with large and complex data sets, Data Vault can provide a robust and scalable solution for managing data in a Data Warehouse. By leveraging the benefits of 3NF and Star Schema, it can help you ensure that your data is accurate, consistent, and easily accessible.

In conclusion, if you’re looking for a data modeling solution for your Data Warehouse, Data Vault is definitely worth considering. With its flexible and scalable architecture, it can help you manage your data more effectively, and using tools like Python and dbt can make it easier to build and maintain your Data Warehouse over time.