Why Organizations Should be Using DBT: A Comprehensive Guide

Data modeling and engineering are essential parts of any big data project. DBT, or Data Build Tool, is a powerful tool that simplifies the data pipeline and enables faster and more accurate data-driven decision-making. 

In this article, we will talk about why organizations should be using DBT Core ™ for their data modeling and data engineering needs. We will also present real-life examples of companies that have successfully implemented DBT and how it has helped them achieve their business goals.

What is DBT?

Have you ever found yourself struggling with transforming and modeling data for efficient analytics? It can be a real headache, especially if you're dealing with large amounts of complex data. Luckily, there's a solution: DBT.

DBT stands for "Data Build Tool", and it's an open-source command-line tool that simplifies your data pipeline by allowing you to transform data in your data warehouse. With DBT, you can create a semantic layer for your data, which makes it easier to work with and analyze. It's compatible with major data warehouses like Snowflake, BigQuery, and Redshift, so you can use it no matter what platform you're working with.

Impact of AI and Machine Learning on Product Analytics

One of the great things about DBT is that it's easy to use. The SQL syntax is simple and easy to read and write, which means you can quickly iterate on your data models. This is especially useful if you're working on a project where you need to make frequent changes to your data models. With DBT, you can make those changes quickly and easily, without having to spend a lot of time on manual updates.

Let's look at an example to illustrate how DBT works. Imagine you're working on a project where you're analyzing sales data for an e-commerce website. You have a large data warehouse with a lot of tables, and you need to create a new table that aggregates sales data by region. With DBT, you can easily create a new model that does this aggregation, and then use that model in your analytics.

Here's what the code using DBT might look like:

-- Create a new model that aggregates sales data by region
select
  region,
  sum(sales) as total_sales
from sales_data
group by region;

With this simple SQL query, you've created a new model that aggregates sales data by region. You can then use this model in your analytics to get insights into how sales are performing in different regions. This is just one example of how DBT can simplify your data pipeline and make it easier to work with and analyze your data.

Features of DBT Cloud

DBT Cloud is a powerful platform that offers a suite of features to streamline your data analytics processes. Whether you're working with Snowflake, BigQuery, Redshift or other data warehouses, DBT Cloud has got you covered. 

Let's take a closer look at some of the key features of DBT Cloud:

Documentation: One of the most helpful features of DBT Cloud is its ability to generate documentation for your data models. This documentation is accessible as HTML files and provides a clear picture of your data models' schema, relationships, and transformations. It makes it easier for other team members to understand and work with your data models.

Data Lineage: With DBT, you can easily trace the flow of your data from source to destination. This is especially useful when you're dealing with complex data pipelines, where it can be difficult to keep track of what's happening at each stage. With DBT Cloud, you have complete visibility into the data lineage, making it easier to identify and fix errors.

Version Control: DBT Cloud also offers version control for your data models, integrating with Git. This ensures that you can track changes to your data models over time and roll back to previous versions if needed. It's an essential feature for collaboration and ensuring that everyone is working with the latest version of the data.

Testing: With the ability to test your data models, you can ensure that your data is accurate and reliable. You can set up tests to validate your data models, making sure that they're producing the expected results. It helps you catch errors early on and maintain the quality of your data.

Semantic Layer: DBT Cloud also creates a semantic layer for your data, which simplifies your data analytics processes. This semantic layer acts as an abstraction layer, making it easier to work with your data. You can define your business logic and metrics in this layer, making it easier to analyze and understand your data.

Source Freshness: DBT Cloud's source freshness feature ensures that your data is always up-to-date. It automatically detects changes in your source data, triggering updates to your data models accordingly. This ensures that your analytics insights are always based on the latest data, eliminating the risk of using outdated information.

DBT Cloud is a powerful platform that offers a range of features to streamline your data analytics processes. Its documentation, data lineage tracking, version control, testing, semantic layer, and source freshness features make it a great choice for teams working with complex data pipelines. 

Advantages of using DBT

Using DBT for data modeling and engineering has several advantages. Some of the key advantages are the following:

Simplifies the Data Pipeline: DBT simplifies the data pipeline by providing a unified framework for transforming and modeling your data. This makes it easier to analyze your data for efficient data analytics.

Let's say you're working with data from various sources such as social media platforms, e-commerce websites, and CRM systems. DBT makes it easier to integrate this data into a single pipeline, so you can analyze it and gain insights into your customers' behavior.

Faster Iteration: DBT allows you to quickly iterate on your data models. The simple syntax used by DBT makes it easy to read and write, which reduces the time needed to make changes to your data models.

Imagine you're a data analyst and you want to test different hypotheses about your customers' behavior. Using DBT, you can quickly create different data models to test these hypotheses without wasting time rewriting complex queries.

Better Collaboration: DBT provides version control for your data models. This makes it easier for team members to collaborate on the same project and track changes made to the data models.

Let's say you're part of a team working on a data analysis project. Using DBT, you can collaborate with other team members and track changes made to the data models. This helps ensure that everyone is working from the same data source, reducing the chances of errors or inconsistencies.

Improved Data Quality: DBT allows you to test your data models, ensuring that they are accurate and reliable. This helps you maintain data quality, which is crucial for making data-driven decisions.

Suppose you're a business analyst and you're using data from different sources to make decisions about your marketing strategy. With DBT, you can test your data models and ensure that your analysis is based on accurate data.

Disadvantages of using DBT

While DBT has many advantages, it also has some disadvantages. Some of the key disadvantages are as follows.

Limited Support for Source Data Formats: DBT has limited support for source data formats, which may require additional transformation steps. This can add extra complexity to the data pipeline.

For instance, let's say you're collecting data from a smart metering system that uses a unique binary format to store power usage data. Since this format is not natively supported by DBT, you'll need to write a custom script or use another tool to convert the data into a format that DBT can ingest. This adds extra complexity to the data pipeline and can slow down the development process.

Requires Knowledge of SQL: DBT requires knowledge of SQL, which may be a barrier for some team members who are not familiar with this programming language.

Let's say you're working with a team that includes members who have no experience with SQL. In this case, they may require additional training or support to work effectively with DBT.

Limited Flexibility: DBT has limited flexibility in terms of data modeling. This means that some complex data modeling scenarios may require additional workarounds or customizations.

Suppose you have a database that stores data in a tree-like structure, such as an organizational chart or a file system. DBT's basic data modeling features may not be sufficient to represent and transform this data in a meaningful way. In this case, you may need to write custom SQL code or use a different tool altogether to handle the hierarchical data. 

This extra effort and complexity can add to the development time and may require specialized skills that not all team members possess.

How DBT can improve the data engineering process

DBT (Data Build Tool) is a powerful open-source command-line tool that helps to simplify the data engineering process. DBT improves the process of transforming, modeling, and managing data for efficient data analytics. With its unique features and core use cases, DBT has become an indispensable tool for many data professionals. 

Some of the key ways in which DBT can improve the data engineering process are as well:

Simplifying the Data Pipeline

One of the main advantages of DBT is that it simplifies the data pipeline, making it easier to transform and model data for efficient data analytics. This enables data professionals to focus on delivering high-quality data, rather than worrying about complex pipelines. 

Enabling Faster Iteration

Thanks to its simple syntax that is easy to read and write, DBT allows for faster iteration of data models. This means that data professionals can quickly test and improve their data models without spending too much time on development.

Improving Collaboration

By providing version control for data models, DBT enables data teams to work together more efficiently. This can lead to better quality data and more accurate insights, as different team members can contribute their expertise to the process.

Improving Data Quality

With its testing capabilities, DBT allows data teams to validate their data models, ensuring that they are accurate and reliable. This helps to avoid costly mistakes and ensures that the data being used for analysis is trustworthy.

​The Importance of Data Modeling in Data Engineering​

Four core use cases of DBT in Modern Data Stack

DBT has four core use cases in modern data stacks, including data transformation, data modeling, data pipeline, and data warehousing. Let us take a look at each of these in detail.

Data Transformation

DBT simplifies data transformation, making it easier to model your data for efficient data analytics.

Data transformation is an important step in the data engineering process. It involves cleaning, filtering, and aggregating data to create a clean and reliable dataset for analysis. DBT simplifies this process by providing a simple syntax that is easy to read and write. With DBT, you can easily create transformations in SQL, making it easier to model your data for efficient data analytics.

For example, let's say you have a large dataset of customer transactions. You want to analyze the data to identify trends and patterns that can help you improve customer experience. Using DBT, you can easily transform the data by aggregating transactions by customer, product, or region. This will give you a clean and reliable dataset that you can use to identify trends and patterns in customer behavior.

Data Modeling

DBT creates a semantic layer for your data, making it easier to analyze and understand.

Data modeling is the process of defining the relationships between data entities in a database. It involves creating a logical schema that defines how data is organized and stored. DBT simplifies this process by creating a semantic layer for your data. This layer defines the relationships between data entities, making it easier to analyze and understand.

For example, you have a database of customer transactions. You want to analyze the data to identify the most popular products and regions. Using DBT, you can create a semantic layer that defines the relationships between customers, transactions, products, and regions. This will make it easier to analyze the data and identify trends and patterns.

Data Pipeline

DBT simplifies the data pipeline, making it easier to transform and model your data for efficient data analytics.

Data pipelines are used to extract, transform, and load data from various sources into a data warehouse. DBT simplifies this process by providing a simple syntax for transforming and modeling data. It also allows you to test your data models, ensuring that they are accurate and reliable.

Let's say you have multiple data sources, such as CRM, marketing, and sales data. You want to combine this data into a single dataset for analysis. Using DBT, you can easily transform and model this data into a single schema. This will make it easier to analyze the data and identify trends and patterns.

Data Warehousing

DBT enables efficient data warehousing, making it easier to store and access your data.

Data warehousing is the process of storing and managing large amounts of data for analysis. DBT simplifies this process by providing a simple syntax for transforming and modeling data. It also allows you to test your data models, ensuring that they are accurate and reliable.

For instance, let's say you have a large dataset of customer transactions that you want to store in a data warehouse. Using DBT, you can easily transform and model this data into a schema that is optimized for analysis. This will make it easier to store and access the data, and also improve the quality of analysis.

DBT Cloud vs. Core

DBT Cloud is a cloud-native platform for building and managing your analytics data pipelines, while DBT Core is an open-source command-line tool. 

The table below highlights the major differences between DBT Cloud and Core.

Success stories of companies using DBT

Data is considered the new oil, and it is not a surprise that companies around the world are looking for ways to extract meaningful insights from their data. Several companies have successfully implemented DBT to improve their data modeling and engineering processes. 

Some of these success stories of the companies are the following:

A Fortune 500 oil & gas company moved away from its enterprise data warehouse towards an agile approach to data management. Instead of relying on a centrally-managed system for updating and modifying the EDW, they chose DBT and Snowflake to enable self-service. This setup allowed users to assemble models, access a fast database, and build models in a language that non-IT people can understand. They also integrated workflow-based release processes.

Aktify, a customer engagement platform, eliminated manual tasks and errors from their data transformations by using the Databricks Lakehouse Platform and DBT. This helped them reduce 80% of their data engineering hours.

A leading technology company, Andela used DBT to centralize its data and streamline its data engineering processes. They were able to reduce their time-to-market by shaving off five months from their product development timeline.

Whatnot - a leading e-commerce company that used DBT to improve its speed and to focus on scaling its team. This helped them to increase their speed by 4-8x to go from idea to production and decrease the maintenance costs by 10x on the same lines.

These success stories demonstrate how companies of all sizes are using DBT to improve their data modeling and engineering processes, reduce errors, and increase productivity.

Comparison of DBT with other data modeling and data engineering tools

DBT has several advantages over other data modeling and data engineering tools. Some of these key advantages are as follows:

Open Source

One of the biggest advantages of DBT is that it is open-source, making it easy to use and accessible to everyone. Other popular data modeling and data engineering tools, such as Informatica and Talend, are not open-source, and users need to pay licensing fees to access their features. 

With DBT, users can download and use the tool for free, and its open-source nature means that users can customize it to suit their specific needs.

Simplifies Data Pipeline

DBT simplifies the data pipeline, making it easier to transform and model data for efficient data analytics. Compared to other tools like Apache Airflow, which require users to write complex code to create and manage data pipelines, DBT offers a simple, declarative syntax that allows users to focus on modeling and analysis rather than the underlying infrastructure. 

This simplification means that users can create and manage data pipelines with less effort, reducing errors and improving the overall reliability of their data.

Creates a Semantic Layer

DBT creates a semantic layer for data, making it easier to analyze and understand. With DBT, users can create a centralized data repository that eliminates silos and inconsistencies in their data, making it easier to analyze and interpret data. Other tools like Looker and Tableau provide similar functionality, but DBT's open-source nature and ease of use make it an attractive choice for businesses of all sizes.

Implements Version Control

DBT implements version control for data models, making it easier to collaborate with other team members. Version control is a crucial feature in data modeling and engineering, and DBT's integration with Git makes it easy for teams to track changes, collaborate on projects, and roll back changes if necessary. 

This feature is particularly important for businesses that have multiple team members working on the same data pipeline.

DBT is a powerful tool for data modeling and engineering that offers several advantages over other tools. Its open-source nature, simplified data pipeline, semantic layer creation, and version control implementation make it an attractive choice for businesses of all sizes. 

While other tools may offer similar functionality, DBT's ease of use and accessibility make it a popular choice for businesses looking to improve their data modeling and engineering processes.

Implementing DBT in your organization

Implementing DBT (Data Build Tool) in your organization can significantly improve your data modeling and engineering processes. However, there are some prerequisites and best practices that you should follow to ensure a smooth and successful implementation.

Prerequisites

Some of the key prerequisites are:

Knowledge of SQL: To implement DBT, you need to have knowledge of SQL, which is a requirement for building data models in DBT. If your team lacks SQL expertise, you may need to invest in training or hiring experienced SQL developers. 

Access to a Data Warehouse: DBT requires access to a data warehouse such as Snowflake, BigQuery, or Redshift to store and manage your data models.

Best practices for implementing DBT

To ensure a successful implementation of DBT, you should follow some best practices that include:

Start Small: It is always best to start with a small project to get familiar with DBT and its features. This approach can help you identify any challenges or limitations before you start working on more complex projects.

Collaborate with Other Team Members:  Collaboration is important when implementing DBT. You should work closely with other team members to ensure that your data models are accurate and reliable. This collaboration can help you identify any gaps in your data or inconsistencies that may impact your analysis

Test Your Data Models: Testing your data models is essential to ensure that they are accurate and reliable. You should test your data models against different scenarios to identify any issues or limitations.

Challenges of Implementing DBT

While implementing DBT can bring several benefits to your organization, there are some challenges that you should be aware of. The major challenges of implementing DBT in your organization are the following.

Limited Support for Source Data Formats: DBT may require additional transformation steps to work with certain data formats, which can be time-consuming and may impact your project timelines. 

Requires Knowledge of SQL: DBT requires knowledge of SQL, which may be a barrier for some team members. To overcome this challenge, you may need to invest in SQL training or hire experienced SQL developers.

Future scope and updates of DBT

DBT (Data Build Tool) is a dynamic tool that is constantly evolving to meet the changing needs of the data engineering community. As a result, it is regularly updated with new features and improvements. The future scope and updates of DBT are promising and include the following:

Conclusion

DBT Core ™ is a powerful tool for data modeling and data engineering that can help organizations streamline their data pipeline, transform data for efficient data analytics, and enable faster and more accurate data-driven decision-making. It provides numerous features such as documentation, data lineage, version control, testing, semantic layer, and source freshness. By using DBT, companies can eliminate data silos, improve the performance of existing infrastructure, and enable simple self-service for analysts, PMs, and other data consumers. 

Datazip, a no-code data engineering platform for the analytics team, will be releasing and supporting DBT Core ™ natively soon as part of the product. If you're interested in learning more about DBT and how it can benefit your organization, be sure to check out Datazip and start transforming your data today!

FAQs

What is DBT and how does it work?

DBT (Data Build Tool) is an open-source command-line tool that enables teams to transform raw data in their data warehouse into analytics-ready data sets. It works by allowing users to define transformations in SQL code, which DBT then compiles and runs in their data warehouse. DBT provides functionality for version control, testing, and documentation of data models.

What are the advantages of using DBT for data modeling and engineering?

Some of these advantages include the following:

• Enables collaboration between data analysts, data scientists, and data engineers

• Facilitates version control of data models

• Offers automated testing of data models, ensuring that changes do not break existing models

• Simplifies documentation of data models, making it easy for new team members to understand the data pipelines

• Allows for continuous integration and delivery (CI/CD) of data models, enabling teams to quickly iterate and deploy changes

Can you provide examples of companies that have successfully implemented DBT?

Many companies have successfully implemented DBT, including:

• Whatnot

• Sunrun

• Reforge

• Aktify

• Blend

• Loft

• Mastery

How does DBT compare to other data modeling and engineering tools?

DBT is a unique tool that focuses on the transformation and modeling of data within a data warehouse. Other data modeling and engineering tools, such as Apache Airflow or Luigi, are more focused on managing workflows and pipelines that include data transformation. DBT provides features that are specifically designed for data modeling, such as the ability to version control and test data models.

What is the estimated cost of using DBT?

DBT is an open-source tool, which means that it is free to use. However, using DBT may incur costs associated with running and maintaining a data warehouse. These costs will depend on the specific data warehouse being used and the resources required to run DBT within that environment.

What are the data storage systems supported by DBT Cloud?

DBT supports a wide range of data storage systems, including the following:

• Amazon Redshift

• Google BigQuery

• Snowflake

• Microsoft SQL Server

• PostgreSQL

• Oracle

DBT also provides a flexible plugin architecture that enables users to extend its capabilities to support other data storage systems.