The expanding world of data is pushing commerce to new limits. The way that data is sourced, transformed, and stored is constantly changing and improving. One tool that is becoming increasingly popular nowadays is dbt Cloud.
If you do not specialize in tech, a lot of these nuanced topics can get quite confusing. The data build tool, more commonly referred to as “dbt”, is an open-source, data transformation technology that allows data analysts and engineers to transform the data stored in their warehouses more efficiently.
Dbt can be used to run and store your SQL code against a database neatly, build out complex data transformation workflow, and write code that is version-controlled and easily digestible. Overall, this tool will improve the collaborative efforts of your company dramatically.
You can also expand the functionality of the open-source Core product with a SaaS component. This puts your scheduler, hosted docs, interoperability, and IDE (integrated development environment) in one central location.
That’s not all, though! Dbt Cloud can be used in a multitude of ways, for a variety of purposes. For the top five reasons to use dbt Cloud, keep reading below!
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In recent years, there have been significant advancements made by the dbt Cloud. Using this technology, you are no longer stuck in the process of setting up a dbt project by using a text editor. Normally, you’d have to build individual templated SQL queries that work with each other, utilizing the dbt command-line tool to gather the SQL (dbt compile), and then returning the models to the warehouse data archive.
The dedicated IDE (integrated development environment) built into the dbt Cloud greatly reduces the issues that stem from this process. This is accomplished in several different ways:
- Lets you preview recent versions of queries with a key command
- Gathers the query
- Moves the query to your data warehouse (limit of 500 attached)
- Shows the outcomes in the same browser tab
- Writes fast and accurate code
- Validates your work every time you save a query
Improve Slim CI
Typically, without dbt Cloud at play, you’d have to undergo a Continuous Integration process where you’d have to construct your complete project, even with just one model with “Slim” CI. With dbt Cloud, you can lower costs and build times by only building and testing the adjusted portions of your project.
This makes things a heck of a lot easier for your analytics engineers. Overall, Slim CI is easier to read and more user-friendly when run with dbt Cloud. It is also more capable of dealing with all of the retention of the run artifacts needed for deferral. It can simply deploy an UI to organize the operation.
Last but not least, dbt Cloud enables you to schedule and trigger tasks to be run automatically every time a pull request gets set up. As a result, the setup process can be entirely managed by a single data analyst.
When you start using dbt, you’ll have access to a built-in documentation viewer that helps you evaluate your project and its needs. With this tool, you can avoid flipping through hundreds of individual documents on different pages.
While waiting for local development in your centralized data warehouse, you can preview your project by running the commands “dbt docs generate” and “dbt docs serve.” With dbt Cloud, you can make your workflow more collaborative by making the documentation accessible to anyone who opens your reports.
This eliminates the need for a ton of unnecessary communication. Instead of your employees having to ask a ton of questions about these reports, they can look at them themselves. Not only does this save time, it also helps reduce miscommunication with other team members.
Another feature of your dbt Cloud account is a hosted documentation site that comes with restricted access. You can establish free viewer accounts and grant access to those who need it.
This way, you do not have to worry about security issues. You’ll see exactly who is using which documents and have an up to date record of their activity.
Dbt Cloud also provides better organization for your data. It arranges all of your data transformations into distinctive models quickly and accurately.
Each dbt model is a distinct statement that can transform raw data into the target dataset or operate as a transitional phase in this type of transformation. The most commonly used information can be structured and used in a format that will work best for efficiency, collaboration, and version control.
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