AUDIENCE: Technical

LEVEL: Basic

You will hard pressed to even find the name of your company when you look at the MAD (Machine Learning, Artificial Intelligence & Data) landscape 2024, let alone trying to find some other company. It is busy and has over 1000s of company in the data space. In this populated world, there is one that stands on its own, dbt (dbt Labs).

What started as a support consulting services, dbt has become an essential tool. It solves the long-standing challenges of traditional data warehousing while embracing modern software engineering principles. In this article, we will discuss why dbt is the future of data engineering and why it will continue to outperform its competitors.

SQL + Modern Software Principles: Simplify Data Pipelines

dbt makes it easy (intuitive) for you to create dbt pipelines. It allows you to transform raw data into dimensional tables using SQL select statements. This is because dbt employs software best practices such as modularity and Don’t Repeat Yourself (DRY) principles.

Unlike traditional data tools that often require learning entirely new programming languages, dbt allows data engineers to stick with SQL while also integrating Python-like syntax through Jinja templating — giving you more flexibility.

The result: A tool that bridges the gap between ease of use and the power of modern software engineering.

One Tool, Endless Compatibility: Open-Source Flexibility Meets Enterprise Power

As an open-source tool, dbt works across all major data warehousing environments like Amazon Redshift, Google BigQuery, Databricks, Snowflake, and more. It even integrates with popular open-source databases like MySQL, PostgreSQL, and DuckDB.

The tool is available in two versions: dbt-Core, which is free and perfect for open-source projects (though it requires a separate database), and dbt-Cloud, a commercial option with added cloud-based functionalities. dbt’s broad compatibility ensures it can be deployed in virtually any data ecosystem, making it the perfect solution for businesses of all sizes.

dbt’s Git Integration: Scale Data Models with Confidence

Any developer will tell how crucial version control is in maintaining system integrity and avoiding breaking changes. With over 90% of developers using Git for version control, dbt’s native integration with Git makes it special. This feature allows data teams to work collaboratively on a single project with full transparency.

In contrast, other major data warehousing tools like Google BigQuery, Amazon Redshift, and Snowflake often don’t prioritize version control in their workflows. dbt’s seamless integration with Git gives it advantage, offering you a reliable way to manage and scale your data models without compromising quality or introducing inconsistencies.

dbt’s Auto-Documentation and Lineage Map: Unlock Data Transparency

One of the most tedious aspects of working with legacy data warehousing systems is creating and maintaining documentation. With dbt, this is not a problem anymore. dbt automatically generates documentation for data transformations, including a comprehensive data lineage map that’s built using metadata stored in YAML files.

This level of standardized, shareable, and intuitive documentation is a game changer. It eliminates the need for developers to manually create their own (often inconsistent) documentation, making it far easier for thier teams to understand and work with data definitions over time.

dbt’s Native Quality Testing: Trust your data with reliable pipelines

Ensuring data quality is a top priority for any organization, and dbt won’t disappoint you here. Unlike other data warehousing tools, dbt emphasizes native testing, allowing you to run data quality checks automatically before each pipeline is executed. Within minutes, developers can create their own custom tests using SQL queries, ensuring that the data pipelines deliver reliable and trustworthy outputs.

This focus on data testing sets dbt apart from competitors, giving you peace of mind that your data transformations maintain high-quality standards.

dbt’s Hidden Hurdles: What You Should Know Before Diving In

While dbt is a powerful tool, it’s not without its limitations. For one, dbt cannot function as a standalone data warehouse. It requires integration with an external database for storage and processing. Additionally, dbt may present a learning curve for those new to the platform, requiring adequate training before users can fully leverage its capabilities. However, with extensive documentation and a growing community of users, dbt’s resources for learning and troubleshooting are robust.

Conclusion

As companies continue to navigate the complexities of data management, dbt stands out as a solution that is both adaptable and forward-thinking. With its strong foundation in version control, intuitive workflows, automatic documentation, vendor compatibility, and native data quality testing, dbt is paving the way for a new era of data engineering.