What Is Data Build Tool (DBT) And How Is It Different?

What Is Data Build Tool (dbt) And How Is It Different From Other Tools?

The volume of data collected today is so extensive that handling it efficiently can be overwhelming. In a constantly changing environment, businesses need to improve how they manage all this data. 

Enter data transformation tools. In an effort to sort, combine, reorganize, filter, and clean up all the data entries, this software can help your enterprise develop useful and reliable insights through analytics and reporting.

In today’s market, several tools can assist you in transforming the data. One that particularly stands out is the dbt, aka data build tool. More and more organizations are recognizing its transformative potential when it comes to overhauling major parts of ETL processes with ease and speed.

Data plays a pivotal role in decision-making for all businesses. The sheer volume of it is so much; it can faze anybody. Handling this staggering amount of data and making it accessible to all becomes increasingly challenging with a continuously changing business environment. Disconnected sources, issues with data quality, and contradictory metric definitions and business elements create chaos. Furthermore, unnecessary efforts and poor quality of distributed information make for poor decisions.

What is the Data Build Tool (dbt)?

In simple terms, a dbt is a developmental framework or a command-line tool that unifies a modular SQL with the most effective techniques in software engineering. It makes data engineering efforts easily accessible to data analysts, helping them become data engineers in the process.

Using this tool, your data analysts will have no problem transforming data stored in your warehouses by using simple select statements. Furthermore, they’ll be able to automate testing and implementation of the data transformation process. Due to their experience with SQL, they’ll be able to leverage dbt tools in order to construct production-grade data pipelines. 

To put it differently, using dbt in your organization will eliminate the skill barrier created by limited staffing resources and poor capacities of legacy technologies.

How a DBT Differs From Other Tools

Consider the following:

• dbt can be easily learned by anyone with previous experience in writing SQL queries and statements. Soon, they’ll be able to construct data models with ease, write tests, and plan processes that deliver actionable, consistent, and reliable analytics-powering datasets.
• Data Build Tooldbt is like a layer of orchestration added on top of your data warehouse which can enhance the efficiency and the sheer speed of your enterprise’s data transformation and integration efforts.
• dbt allows you to perform all calculations and computational operations at the database level, ensuring that data transformation is completed at the highest efficiency and security, while also maintaining the integrity of the data contained within. 

So how does one make the best of a seemingly impossible situation? By transforming your data. It will allow you to clean, combine, remove duplicates, reorganize, and filter all your data. The transformation will enable your enterprise to develop useful and reliable insights via analytics and reporting. 

Today’s markets offer several tools to achieve data transformation. Yet, the one that clearly stands out, in particular, is dbt or the data build tool. The dbt tool will help you achieve the transform part of the ETL (extract, transform, load) process with relative ease and speed.

1. Data Build Tool supports several databases like Snowflake , BigQuery, Postgres, Redshift, etc., and is easy to install using Python Package Installers or pip as it is more often called.

2. dbt is an open-source application written in Python, giving the users the power to customize it as needed.

3. A dbt user only needs to focus on writing select queries or models to reflect the business logic. You don’t have to write sections of repetitive code that are used occasionally with no variations. To be precise, it does away with the need for writing boilerplate code for creating tables and views and specifying the execution order of the written models. dbt takes care of it by:

4. It also offers a lot of flexibility to the users. Say, for example, the resultant project structure is not a match for your organizational needs. You can customize it by editing the dbt_project.yml file or the configuration file and rearranging the folders. 

What is Data Build Tool (DBT) Advantages

What is Data Build Tool (DBT) Disadvantages

DBT - Best Practices

To make the most out of a data build tool, here are some of the best practices you should be aware of:

• Use the ref function

The ref function makes dbt very useful as it allows you to infer dependencies, which sees to it that all the models are generated in the best order. This function also means that your current model draws mainly from views and upstream tables.

• Only limit references to raw data

For the most part, dbt projects rely on raw data loaded by third parties. Hence,  the structure can drastically change over time as new columns or tables are added or edited, making it a lot simpler to update models if the references are limited to raw data. 

• Break down complex models into simpler chunks

Generally speaking, complex models will include several CTEs. A dbt allows you to separate CTEs into completely independent models built on top of one another. You should simplify complex models if:

1. A CTE is duplicated across multiple models 
2. A particular CTE modified the grain

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Data Build Tool is the right choice for people interacting with data warehouses like data analysts, engineers, or scientists. To make full use of its exceptional capabilities, having knowledge of basic programming, especially “if statements” and “for loops,” will come in handy. The dbt tool allows data experts to transform the data stored in the organization’s data warehouses more effectively. They can test the transformation process and deploy modifications to visualize the needs every step of the way. Dbt shows you the manner in which data flows through the enterprise, all the while enriching the outcomes from other data and analysis technologies.

Start your data transformation journey now!

The data build tool is the right choice for those interacting with data warehouses who can’t afford to waste months on end training their data analyst in ETL. In fact, you only need basic programming knowledge such as ‘if statements’ and ‘for loops’ to utilize it to great effect.

By implementing it in your organization, you can transform the data stored in your data warehouses and efficiently test the entire process while also easily deploying any necessary modifications. Ultimately, you’ll be able to make better use of the data you acquired and enrich outcomes from the use of other data analysis technologies.

Data and analytics are what Oamii Technologies does and does the best. If your business is in need of our expertise, feel free to contact us at 561-228-4111 . Our consultants will help you build a solid foundation to erect your ladder of success. Now is the time to undertake an enterprise data initiative to fuel your growth.

FAQs 

What is dbt (Data Build Tool)?

It’s a developmental framework that makes data transformation fast and reliable by combining modular SQL with software engineering processes. It allows those with a rudimentary knowledge of data analysis to build complete data pipelines. 

Why use dbt instead of SQL?

The main reason why you should use a data build tool instead of SQL is the improved workflow. For instance, a dbt has built-in parameters for testing the code and, more importantly, uses an implicit lineage DAG. It also provides reusable macros and is integrated with code repositories.

Is the data build tool good?

Absolutely.

A data build tool is very useful as it provides data analysts with more control over the entire analytics workflow, allowing them to write data transformation code while also helping them complete deployment and documentation.

It supports well-organized data that is ready for analysis by using simple SQL SELECT STATEMENTS without relying on boilerplate code. 

What is the most efficient way to organize dbt models?

The best way of organizing your dbt models is into two categories/folders: marts and staging. 

The goal of staging models is to read information from raw data that necessitates data cleaning. Mart models, on the other hand, are more complicated and contain complex logic, joins, and aggregations. In other words, this folder contains the end product.

How to set up a dbt and Snowflake connection?

Connecting the data build tool to Snowflake is relatively straightforward:

1. Create a Snowflake account 
2. Create a dbt account
3. Start a new project by clicking the start button
4. When prompted, select Snowflake as a data warehouse
5. Configure the connection by choosing authentication methods and enter the name of the Snowflake account and its URL
6. Connect dbt with a GitHub repository
7. Create a new repository by using the same email address on the dbt cloud account and select GitHub on the repository screen 
8. Click on the linking options on the GitHub integrations page
9. Select the dbt repository and click install

Once you return to the home page, you’ll see your repository link