SQL for Data Transformation: From Excel to Expert

The open-source tool dbt, developed in 2016, supports data teams by optimising SQL for data transformation, simplifying their workflows. Today, dbt has become widely adopted among data engineers and analysts, with a thriving community of over 20,000 users. Many professionals now view data analysis in terms of ‘before dbt’ and ‘after dbt.’ As ELT (Extract, Load, Transform) processes gain traction alongside cloud storage, SQL for data transformation is becoming indispensable. Tools like Fivetran store data efficiently, turning raw information into actionable insights.

Why SQL for data transformation is essential in dbt

To understand dbt’s impact, let’s explore its functionality. dbt, short for data build tool, enables teams to deploy analytics code with speed. dbt transforms SQL from an analytical tool into an engineering resource. Essentially, dbt converts raw data into structured, actionable information.

How dbt works

Now that we know dbt’s purpose, let’s break down its workflow.

1. Data Extraction

In the initial step, dbt pulls data from various sources, ensuring compatibility across platforms.

2. Data Transformation

dbt then performs its core function: transforming raw data into an organised, ready-to-analyse format.

3. Data Loading

After transforming data, dbt loads it into data warehouses, such as Databricks or Snowflake. This ensures data remains accessible for analysis.

4. Scheduling

Finally, dbt enables regular updates, whether hourly or daily, ensuring data remains fresh and reliable.

Building best practices with SQL for data transformation

dbt integrates smoothly within data ecosystems, offering clear insights. For instance, it pulls marketing data to display customer acquisition costs across platforms. dbt follows development best practices through version control, testing, documentation, and collaboration.

Version Control and CI/CD

Version control organises code updates, while CI/CD facilitates testing, ensuring consistent and error-free code.

Testing and Documentation

Testing prevents errors from reaching production, while documentation clarifies code functionality. dbt allows users to test and document directly in SQL files, maintaining order.

Collaborative Development

By integrating with tools like Git, dbt supports collaboration, enabling teams to work on code without conflicts.

Additional dbt components

Beyond best practices, dbt includes three more components that boost efficiency.

• Modularisation: dbt encourages breaking pipelines into testable modules, simplifying troubleshooting.
• Documentation: Markdown documentation enhances team understanding of data processes, improving usage.
• Data Validation: dbt validates data throughout pipelines, catching errors early to ensure accuracy.

How the community drives SQL for data transformation in dbt

Community plays a key role in dbt, fostering collaboration through dbt Slack, forums, GitHub, and events. The dbt Slack channel connects over 35,000 analytics engineers for real-time assistance. Forums provide in-depth technical discussions, while GitHub offers shared code and models, saving organisations time.

Real-world benefits of SQL for data transformation

JetBlue’s Transformation Journey

For airlines, managing data is challenging. JetBlue faced bottlenecks in its centralised data teams. With dbt, the airline empowered its teams to co-manage data. This shift improved reporting uptime to 99% without raising costs. Within three months, JetBlue migrated 26 data sources and 1,200 models to dbt, building a strong data foundation.

Building a Modern Data Stack

JetBlue’s new data stack now includes Snowflake, Fivetran, and dbt Cloud. This system enables JetBlue to reimagine its data strategy, laying a solid foundation for data analysts.

dbt pricing

dbt offers flexible pricing, with three tiers: Developer, Team, and Enterprise.

• Developer: Perfect for individuals, this free plan includes job scheduling, unlimited runs, and GitHub support.
• Team: At $100 per seat monthly, it supports advanced features like SQL proxy and API access.
• Enterprise: This premium option offers custom deployment and robust security, ideal for large organisations needing full support.

Conclusion

In today’s data-driven landscape, data transformation tools like dbt are crucial for decision-making. dbt provides a cost-effective SQL solution that simplifies raw data into actionable insights, optimising workflows for data engineers and analysts.ata into useful insights. Your data engineering and analyst team will become more streamlined as a result.

How can I learn more?

This article is part of a greater series centred around the technologies and themes found in the first edition of the TechRadar by Devoteam . To read further into these topics, please download the TechRadar by Devoteam