We all love shortcuts. That’s why many organizations use stored procedures in their data warehousing processes. They’re an excellent solution for packaging and scheduling complex transformations through logical conditions. Stored procedures have become a core building block of teams’ workflows. But do they have any cons?
Data workers indicate a noticeable increase in data warehouse costs, frequent data downtime, and a rise in the unavailability of data in production. This immediately translates to confused teams who find it hard to trust their data and even more confused and overworked developers.
dbt is a wonderful tool that helps in transforming data in real time by just using SELECT statements. Dbt takes these inputted statements and turns them into views and tables. Based on this principle, dbt solves major problems in the data industry, and it works as an effective alternative to stored procedures.
The Drawbacks of Stored Procedures
At first, stored procedures make complete sense but their disadvantages become apparent later on when we expect data pipelines to assist in sophisticated but necessary processes such as testability, transparent documentation, and code reusability. Stored procedures aren’t testable since they don’t function well in the documentation data flow, which affects documentation negatively as well.
Stored procedures could turn into a source of stress for development teams as their intermediate steps aren’t obvious and most of the time you’ll find the same code repeated in more than one stored procedure. This lowers the efficiency of your teams massively. To be more practical, stored procedures could be the reason why it’s difficult to trace the primary origins of data in production reports and why your dashboards don’t refresh in a timely manner.
The Benefits of dbt as a Stored Procedures Alternative
Modularity is key when working with dbt. Business objects are managed in separate models and organized into layers which make the data easier to be consumed and namely more testable and self-documenting. Also, this approach minimizes data duplication and simplifies code reuse.
Furthermore, dbt supports version control integration which helps data workers implement and test changes with git-based tools in their transformation pipelines in just a few clicks. We can say that the two most straightforward benefits of this migration are:
1. Solutions to New Use Cases
Dbt uses a real-life scenario of a dbt Cloud use to illustrate the potential of this migration. As a result, after moving from using stored procedures to using dbt, the team could work with new use cases such as real-time data reporting with lambda views.
2. Uptime Enhancements
The refreshing time can slow down whole processes in an organization if it doesn’t match the standards of other systems and clients’ expectations. After migrating from using stored procedures to dbt, the team we mentioned was able to spend less time on pipeline refreshes, improving the uptime to 99.99% (from 65%).
Regardless of the SQL dialect, you’re working with (BTEQ, T-SQL, PL/SQL, etc.), the entire migration process can be summarized into four main steps. You can find this detailed step-by-step process in dbt’s article on migrating from stored procedures. But for you to have a clearer idea of how the final results, we found this illustration:
Migrating from stored procedures to dbt is definitely a much-needed investment if you want to help your teams become more productive and solve data testing and traceability issues. Your pipelines will get more elegant, clean, and resilient to frequent changes. Modular, well-documented, and testable code contributes to the improvement of all areas of your business.
At Blue Orange Digital, we assist our clients from a wide range of industries including IoT, Real Estate, and Retail, in implementing measurable and more effective cloud solutions for their infrastructures. Schedule a free call with our team today to learn how we can help your organization.