Introduction: What Is dbt?

Two common approaches to enable the flow of big data are ELT (extract, load, transform) and ETL (extract, transform, load). Both start with unstructured data and despite the slight difference in naming, they result in distinct practices of data engineering:

• ELT prioritizes loading and keeps transformation for a later time. It would deal with basic pre-processing such as removing duplicated data or filling missing values before serving to a team who is supposed to transform it.
• ETL focuses on transformation before delivering to target systems/teams. Hence, it does more than preprocessing in ELT that would involve making data structured, clean and type transformed.

dbt is a tool to conduct transformation (“T”) practices on data warehouses for ELT. As the name suggests, it involves the operations after the data is extracted and loaded. In other words, once you have “landed” your big data on a data warehouse, dbt can help you to pre-process before serving for the use of subsequent processes. The visualization given below shows its role in a data pipeline.

Tutorial for BigQuery Transformations

dbt is originally a command line tool but currently it has a cloud service (www.getdbt.com) as well that makes the initial steps more convenient for newbees. In this short tutorial, dbt cloud service is used to conduct some basic transformation tasks on the data uploaded from a public dataset (the process was explained here) as a part of Data Engineering Zoomcamp (by DataTalks.club) Capstone Project.

Step-1: Initiate a project on dbt cloud

The process starts with initiation of an account and a project on dbt cloud that is free for individuals:

Step-2: Match with BigQuery

Clicking on the “Create Project” is followed by simple questions to declare project name and data warehouse for integration. Selecting BigQuery as the data warehouse, you will land on a page to assign GCP service account information. (Note that the account has to have BigQuery crenetdials.)

Here, downloading the service account information as a Json file from GCP and uploading it to the dbt would prevent error. Testing the authorization, one can continue for matching the repository to host the dbt project. It is possible to host it on a “dbt Cloud managed repository” or on another repo of choise. Completeing the step, you will have a project ready to initialize on dbt cloud. Clicking on “initialize your project” button, you will have a fresh project template:

Step3: Identify the requirec components and configurations

For a dbt project, core elements are:

• dbt_project.yml file that is to configure the project,
• models folder to host models to be run for proposed transformations,
• macros to invove files to declare reusable SQL queries in Jinja format,
• seeds folder to host CSV files for declerations regarding data on data ware house such as zip code & city names, employee ID & personal data’ etc. Note that these are not the data itself but the references to use it properly.

Step4: Define model

In this introductory tutorial, we will only use the models. The task is to unify all daily data of a PV system produced during a year. That is to say unify 365 files. The source of the data and how they were uploaded to BigQuery was explained in a previous post. The content of the each table can be unified with UNION ALL query as below:

-- Select columns of interest
SELECT measured_on, system_id, \
    ac_power__5074 as ac_power, \
    ambient_temp__5042 as ambient_temp,
    kwh_net__5046 as kwh_net, \
    module_temp__5043 as module_temp, \
    poa_irradiance__5041 as poa_irradiance, 
    pr__5047 as pr 

-- Decleare one of the files to be combined 
FROM `project_name.dataset_name.table_name`   

UNION ALL 

SELECT ...
    ...
    ...
    ...

However, writing 365 lines is not convenient, of course. Hence, it is possible to take advantage of a property of BigQuery. It helps you synthesize long queries with a abviously repetitive pattern. For the case study of the tutorial, it was possible to produce a 365 lines of UNION ALL query for a year by runing the following query on BigQuery:

SELECT string_agg(concat("SELECT measured_on, \
    system_id, ac_power__5069 as ac_power, \
    ambient_temp__5062 as ambient_temp, \
    kwh_net__5066 as kwh_net,   \
    module_temp__5063 as module_temp, \
    poa_irradiance__5061 as poa_irradiance, \
    pr__5067 as pr  \
    FROM `YOUR-BQ-PROJECT-NAME.pvsys1433.",  \
    table_id, "`") , "   UNION ALL \n")  \


FROM YOUR-BQ-PROJECT-NAME.pvsys1433.__TABLES__;

Than, you can simply copy-paste the long query to the model file created on models/staging folder of your project in dbt cloud. Running model with dbt run your_model_name.sql command, you will recieve a new table on your corresponding BigQuery project dataset with the name of your model file.