Venturing into Complex Techniques with dbt: Dynamic Data Selection, Lineage, and Governance

Data Build Tool

DBT (Data Build Tool) is an open-source command-line tool that helps data analysts and engineers transform and manage their data pipelines. It provides a simple and powerful way to define, run, and test complex data transformations in your data warehouse. DBT has become increasingly popular among data professionals due to its ability to enable a more agile approach to data modeling and its focus on testing and documentation.

DBT offers several advanced features that allow users to optimize and streamline their data pipelines. Dynamic data selection allows for flexible querying of tables and views, while Data lineage provides a clear understanding of how data is transformed and used across an organization. Data governance is also critical in ensuring data quality, and DBT offers features for implementing and enforcing data governance policies.

Great, lets discuss on the above topics folks!

Dynamic Data Selection

Dynamic data selection refers to the ability to dynamically select data based on certain conditions or parameters. In dbt, this can be achieved using variables and macros.

For example, let’s say we have a table that contains sales data for multiple countries, and we want to select only the data for a specific country. We can use a variable to define the country we want to select and then use that variable in our SQL query.

Here’s an example of how to use a variable to dynamically select data in dbt:

-- Define the variable
{% set country = 'USA' %}

-- Select data for the specified country
SELECT *
FROM sales
WHERE country = '{{ country }}'

In this example, we define the country variable using the set statement, and then use that variable in our SQL query using Jinja templating syntax.

We can execute this code using dbt by including it in a model file, such as sales_by_country.sql, and then running the dbt run command. This will execute the query and generate a table containing the sales data for the specified country.

Dynamic data selection is useful because it allows us to write flexible and reusable code that can be easily modified to select different subsets of data. For example, we could modify the country variable to select data for a different country without having to rewrite the entire query.

Another example of dynamic data selection is using macros to generate SQL queries based on input parameters. For example, we could define a macro that generates a SQL query for selecting sales data based on a date range:

{% macro select_sales_by_date_range(start_date, end_date) %}
SELECT *
FROM sales
WHERE date BETWEEN '{{ start_date }}' AND '{{ end_date }}'
{% endmacro %}

We can then call this macro in our SQL query and pass in the start and end dates as arguments:

{% from macros import select_sales_by_date_range %}
{{ select_sales_by_date_range('2022-01-01', '2022-03-31') }}

This will generate a SQL query that selects sales data for the specified date range. We can execute this code using dbt in the same way as the previous example.

Dynamic data selection is a powerful feature of dbt that allows us to write flexible and reusable code that can be easily modified to select different subsets of data. By using variables and macros, we can create dynamic SQL queries that can be executed using dbt to generate tables containing the desired data.

Data Lineage

Dag to see relationships

Data lineage is the process of understanding the origin, transformation, and movement of data throughout your organization. It’s important to understand data lineage in order to ensure data accuracy, compliance, and to troubleshoot issues. dbt provides built-in functionality for data lineage, allowing you to trace data from its source all the way to the end user.

The key components of data lineage in dbt are:

• Sources: The data source where your data originates from.
• Models: The transformations applied to your data.
• Analyses: The output of your models used for reporting and analysis.

Here’s an example of how to view data lineage in dbt using the built-in dbt lineage command:

$ dbt lineage --models my_project.my_model

This command will show the lineage of the my_model model in the my_project project. You'll see a graphical representation of the data lineage, showing the sources, models, and analyses used in the transformation process.

In addition to the dbt lineage command, dbt also provides the dbt docs generate command, which generates documentation for your project. This documentation includes a data lineage diagram for each model, showing the sources, transformations, and analyses used in the model.

Lets look at a sample example. Let’s say you have a dbt project that consists of a few models: raw_orders, clean_orders, and orders. You're seeing some unexpected results in your orders model, and you want to use data lineage to troubleshoot the issue.

First, you’ll want to run the dbt lineage command on the orders model to see the data lineage:

dbt lineage orders

This will output a graph of the data lineage for the orders model, showing how the data flows from the source all the way to the orders model.

Next, you’ll want to look for any potential issues in the transformation process. You can do this by examining the SQL code for each of the models in the data lineage graph.

For example, let’s say you notice that the clean_orders model is performing an aggregation that may be causing the unexpected results. You can examine the SQL code for the clean_orders model by running:

dbt source snapshot --select clean_orders

This will output the SQL code for the clean_orders model. You can then examine the SQL code to see if there are any issues with the aggregation.

By using data lineage to trace the data from its source all the way to the analysis, you can quickly identify any issues in the transformation process that may be causing unexpected results. This can save you time and effort when troubleshooting issues in your dbt projects.

Data Governance

Data governance is a crucial aspect of any organization that deals with data. It refers to the management of the availability, usability, integrity, and security of the data used within an organization. Dbt offers features that allow for the implementation of a robust data governance system.

One of the ways dbt enables data governance is through the use of macros. Macros are reusable pieces of code that can be used across multiple models. By using macros, organizations can enforce consistent business logic and ensure that all data transformations adhere to predefined rules and standards.

Another feature of dbt that supports data governance is the use of tests. Tests are code snippets that validate the data in your models against predefined expectations. By defining tests for your models, you can ensure that the data is accurate and consistent across your organization.

Let’s take an example of a data governance system in action in dbt. Suppose you have a requirement to ensure that all your customer data is anonymized. To enforce this, you can define a macro that replaces all customer names with random strings. This macro can then be called from all models that deal with customer data. Additionally, you can define a test that validates that no customer names are present in the final output of your models.

Here’s an example code snippet for the scenario described above:

{% macro anonymize_customer_names(input_cols) %}
{% for col in input_cols %}
  {% if col.name == "customer_name" %}
    CAST(FLOOR(RANDOM() * 10000) AS TEXT) AS {{ col.name }}
  {% else %}
    {{ col.name }}
  {% endif %}
{% endfor %}
{% endmacro %}
-- Sample model using the anonymize_customer_names macro
-- Assumes the "customer_name" column is present in the "customers" table
-- and needs to be anonymized
select
  {{ anonymize_customer_names(ref('customers').columns) }}
from {{ ref('customers') }}
-- Test to ensure no customer names are present in final output
-- Assumes the output table of the "customers" model is named "anonymized_customers"
{% set output_cols = ref('anonymized_customers').columns %}
{% set customer_name_col = output_cols.get('customer_name') %}
{% if customer_name_col %}
  {{ error("Customer name column found in output table!") }}
{% endif %}s

In the example above, a macro anonymize_customer_names is defined that replaces the customer_name column with a random string using the RANDOM() function in Snowflake. This macro is then called from a model that selects from the customers table, which assumes the presence of a customer_name column that needs to be anonymized.

Moreover, a test is defined that checks whether the output table of the customers model contains a customer_name column. If such a column is found, an error is thrown. This helps enforce the data governance policy that all customer names should be anonymized.

Implementing data governance in dbt can help ensure data quality and consistency across an organization, as well as compliance with regulatory requirements.

And now you know how to implement data lineage & governance in dbt.

With the increasing importance of data-driven decision making, having a robust data modeling and transformation tool like dbt is crucial. The topics we covered — Dynamic data selection, Data lineage, and Data governance — all contribute to unlocking the full potential of dbt. By mastering these topics, you can create more sophisticated and scalable data pipelines, improve data quality and accuracy, and ensure that your team is working efficiently and collaboratively. With dbt, the possibilities for data transformation and modeling are endless, and the benefits for your organization are limitless.

Great things are in store for dbt users! New features that will continue to enhance your experience and help you unlock the full capability of your data. Watch out for my next blog on other techniques!!

Explore my other articles on dbt to get started with

Introduction to dbt — Step by Step Guide for Beginners

Exploring about Macros & Ref in dbt

Materialization & Custom Transformations using dbt

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