When setting up a new cloud infrastructure or data organization, a data build tool or DBT should be part of your process. Companies everywhere have started adopting a DBT cloud to help their analytics teams more effectively leverage their time by utilizing software engineering practices. When it comes to managing data, the ever growing and extensive amount that businesses have to sort through should make finding a good way to manage it a top priority.
What Is DBT?
A data build tool helps to make data engineering actions more user friendly to those with standard data analyst skills. It allows the analyst to convert the data from the source by using simple and select statements which helps create the data transformation through code. You will be able to code custom elements for your businesses unique needs by putting the data quality testing on autopilot, implementing the codes, and then distributing the data and the documentation alongside the code. Companies also like it for its built in testing for data quality, reusable macros and its online searchable data catalog.
Essentially, a DBT allows your data analysts to become engineers in their own right. Anyone familiar with the system can create production-level data pipelines. This can help you to consolidate roles in your businesses by eliminating the need for highly skilled data engineering positions. Or you can use the DBT to free up the time and schedules of your engineers so they can focus on more important work.
What Is A DBT Cloud?
So now that you have an understanding of what a data build tool is, let’s look at what a DBT cloud is. While your DBT is essentially for transforming data from your data warehouse, the cloud is a web-based application with an integrated development environment or IDE that helps data teams to configure, deploy, and control the versions of DBT projects. Having a dedicated IDE will help cut down on data processing conflicts. It allows codes to be written in a speedy and efficient manner and can easily compile the query and can transfer it to your data warehouse. The application also allows you to view logs, schedule DBT jobs, share documentation, and continue to integrate and deploy new data in a low maintenance way.
What To Know Before Implementation
Before you start using a DBT cloud, there are a couple things you need to understand. First you need to be familiar with Git. Git is the software used in DBT core. It functions by tracking alterations to files and organizes tasks among programmers who are working on developing the same source code. Anyone working with DBT should learn how to use Git Workflow, Git Branching, and Git for teamwork.
You also need to understand the language that DBT uses to transform data. This language is referred to as SQL. If you do not understand SQL, you will not understand the data transformations that are being done under your DBT. If you know you will be implementing a DBT, make sure you get anyone on your team who will be working with it is proficient in SQL.
The last thing that you need to understand before implementing a DBT cloud is modeling. Modeling is essential when it comes to the coding being reusable and having the ability to transform the data into the structure of the company. This will allow you to optimize the performance of your analytics team.
Getting Started
When you have your DBT cloud up and running, you can effortlessly magnify the collaborative process by making it easily accessible to whoever would like to access your data and reporting. This helps to cut down on human error and miscommunication. You can have complete control of the data access by using a hosted site for documentation. DBT has a built-in documentation site that already comes equipped with access restrictions. You can set up free viewer accounts so you can have control over who the documentation is made available to. This is how security issues and deployment logistics are addressed by the data build tool.
You will also be able to set up your DBT cloud to run automatically in certain areas. The cloud makes it possible to schedule and trigger a stream of essential tasks that need to run each time a data pull request is set up. This is yet another reason DBT clouds are praised for their efficiency.
All in all, the data build tool is a highly effective piece of software that allows for increased productivity, better workflow, and efficient collaboration throughout your entire team. Turn your data analytics team into engineers by implementing this new and highly effective software today. Data storage and management doesn’t have to be an entire department on its own, so let your DBT cloud do the hard work.
