First and foremost, Data Build Tool, more commonly referred to as ‘dbt,’ is an open-source data transformation tool that enables data analysts and engineers to transform the data stored in their warehouses more efficiently, giving them the ideal software development practices.
Customary uses for dbt include running and storing your SQL code against a database in an organized manner, building out complicated data transformation workflows, and writing code that is version-controlled and simple to engage in collaboration with.
Then with dbt Cloud, you can expand the functionality of the open-source Core product with a SaaS component that combines a scheduler, hosted docs, interoperability, and IDE (integrated development environment).
Let us dig into the top five benefits of data teams and analytics engineers using dbt Cloud.
1. Dedicated IDE
With the advancements made by the dbt Cloud, you are no longer stuck in the process of establishing a dbt project by using a text editor to build individual templated SQL queries that rely on one another, using the dbt command-line tool to gather the SQL (dbt compile), and then returning the models to your warehouse data archive.
The dedicated IDE (integrated development environment) associated with the dbt Cloud drastically diminishes the conflict from this process in several ways:
- Allows you to preview recent versions of queries with a key command
- Compile the query
- Transfer the query to your data warehouse (limit of 500 attached)
- Display the outcomes in the same browser tab
- Write fast and correct code
- Validate your work whenever you save a query
2. ‘Slim’ CI
Instead of the typical Continuous Integration situation where you construct your complete project when you only included one model with “Slim” CI, you can lessen costs and build times by only building and testing the adjusted portions of your dbt project. This makes work a whole lot easier for analytics engineers.
The deployment of Slim CI is more simplified and user-friendly with dbt Cloud. In addition, it is capable of dealing with all of the retentions of the run artifacts required for deferral immediately with an easy UI to organize the operation.
Additionally, dbt Cloud makes it possible to schedule and trigger tasks to be run automatically each time a pull request gets set up. Thus, the setup process can be entirely managed by a data analyst.
3. Hosted doc site for documentation
When you begin using dbt, there is a built-in documentation viewer to get a clear picture of your project and its needs. During local development in your centralized data warehouse, you can preview your project by running the commands dbt docs generate and dbt docs serve.
But with dbt Cloud up and running, you can magnify the collaborative process by making the documentation accessible to whoever opens your reports. This cuts out a lot of unnecessary communication and miscommunication with other team members who would have had to ask questions regarding these reports.
One of the many features of your dbt Cloud account is that you receive a hosted documentation site that comes equipped with restricted access. By having free viewer accounts established, you have the authority to make documentation available to those that need it the most without having to be concerned about security issues and deployment logistics.
4. Utilizing iFrames
One of the many beauties of dbt is that it quickly arranges all of your data transformations into distinctive models.
Every dbt model is a separate statement that can alter raw data into the target dataset or operate as a transitional phase in this type of transformation. The information used most often can be structured and emerge in a format that will work best for efficiency, collaboration, and version control.
5. Metadata APIs
Another of the many benefits of dbt Cloud is that it offers metadata APIs, which enable third parties to showcase the clarity and newness of dbt’s data sources. At the moment, those tools need dbt Cloud to offer them a dependable endpoint to get a hold on this information because dbt Core manifests the same artifacts but doesn’t have a method to show that they exist externally.
In conclusion, dbt is an effective tool for analytic workflows that establishes a platform for the sharing of knowledge and cross-functional collaboration to occur. When you maximize dbt with dbt Cloud, now your team can push time-wasting distractions to the side and focus on the ways in which your data can be maximized to create value.
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