In 2025, the global economic landscape remains unstable due to various factors. We have persistent inflation and volatile markets, among many other economic indicators that affect our economy in one way or another. This causes a growing pressure to maximize efficiency, reduce costs, and at the same time maintain the quality of the products we offer with our work. Every penny counts, especially for small and medium enterprises, and the data, the engine of modern decisions, is not an exception. However, managing data can be costly: software licenses, large technical equipment, and manual processes that consume time and money. Will there be any good news? Tools like DBT and cloud warehouses are revolutionizing how companies handle their data, offering significant savings without sacrificing quality.
Costly Licenses, scarce personnel, manual processes
Data handling in 2025 comes with economic challenges that cannot be overlooked: Many traditional data tools have license costs that have grown exponentially. In 2025, an average license for a medium-sized company can exceed £ 50,000 per year, according to estimates based on various current economic studies. This is unsustainable for small and medium enterprises with tight budgets. We can also note the scarcity of personnel. The demand for data engineers and ETL specialists has skyrocketed, salaries have skyrocketed since the specialists who possess the knowledge, overvalue their prices due to the high demand for work that this sector presents. In addition, the global shortage of technical talent makes hiring a large team a luxury few companies can afford and sustain. Another challenge that companies present economically today is that they still depend on manual processes to clean, transform and load data. This is not only slow; an analyst can spend up to 80% of their time preparing data, but is also prone to making mistakes. An error in a financial report can cost thousands of dollars in wrong decisions. From my experience, I know that young, junior- to mid-level analysts, who may seem cost-efficient, do not understand the impact of their mistakes.
In this context, companies need solutions that eliminate these costs without compromising results. That’s where DBT and the cloud do their jobs. How do these tools—DBT and the cloud—help us save with SQL, existing warehouses, and automation? DBT uses SQL, a language that most analysts and data professionals already know. This eliminates the need to learn complex tools or pay for high-cost software. In 2025, when premium software licenses have risen 15% compared to last year, 2024, using a general language like SQL reduces costs drastically. For example, a company that previously spent $30,000 on ETL licenses can replace them with DBT Core, which is free, without losing its functionality.
DBT operates directly on cloud warehouses like Snowflake and BigQuery, which many companies already use. These tools have been adopted in 2025 by most medium and small companies, which means you don’t need to invest in new infrastructure. DBT leverages the power of these warehouses to run transformations, eliminating the need for own servers or additional software. According to current economic estimates, this can save up to 20% in infrastructure costs.
DBT automates the data transformation process, from cleaning to creating analytical tables, instead of relying on a team of 5 engineers to handle manual pipelines, a small and medium company can do it with 1 or 2 analysts who use DBT. For example, a pipeline that used to take 10 hours of manual work can be reduced to 1 hour with DBT, saving up to £500 per week in labor costs (assuming an average salary of £50/hour). In addition, DBT includes automated tests that reduce costly errors, saving potentially thousands in fixes. In summary, DBT and the cloud eliminate three main barriers: costly licenses, large teams, and inefficient processes.
How does this look in practice?
Practical case: An SME saves 30% with DBT in 2025—an SME in e-commerce in the UK with 50 employees that sells products online. In 2024, it spent £60,000 a year on its data strategy: £30,000 on licenses for a traditional ETL tool, £25,000 on a part-time data engineer, and £5,000 on local servers to process data. In addition, the manual processes generated errors that cost £2,000 a year in miscalculated returns.
The SME decides to adopt DBT and the cloud:
– Migrates its data to BigQuery, which it uses to store sales. This costs £0 additional since they leverage their existing subscription.
– Implements DBT Core (free) to transform data. An existing analyst (who earns £40,000 a year) learns DBT weekly using free online resources.
– Automates pipelines with DBT. What used to take 10 hours weekly now takes 1 hour.
Results: Eliminates the ETL license (£30,000/year).
-Does not renew the part-time engineer (£25,000/year).
-Shuts down local servers (£5,000/year).
-Reduces errors with automated tests, saving £2,000/year.
Total saved: £62,000. New cost: £44,000 (analyst’s salary and BigQuery subscription). Net savings: 30% (£18,000).
This case shows how DBT and the cloud can contribute to the data economy for an SME, allowing it to compete without breaking its budget and maintaining its quality.
Conclusion:
In 2025, economic pressure makes saving on data not a luxury but a necessity. DBT and cloud warehouses offer this practical solution to help many SMEs: they eliminate expensive licenses with SQL, leverage existing infrastructure, and automate processes to reduce staff and errors. As we saw, an SME can save up to 30% without sacrificing quality.
