In today’s digital age, technology dominates the economic landscape. Organizations that want to remain competitive in the virtual and digital marketplace have to adopt the new technologies that come into existence. This is especially the case as our society becomes more and more digitally integrated and reliant on technology. The era of technology and information has been upon us for at least a couple of decades now, but what uniquely makes the virtual economy so viable is the growing presence of digital natives in the consumerbase. A digital native is simply someone who grew up alongside the presence of world-changing technologies like the internet and smart-phones. Because of this, they don’t have a perception of what time was like before these tools existed.
It’s a fundamentally different society that kids are growing up in today, which makes them think, socialize, and even enter the workforce in fundamentally different ways. Understanding the modern day consumer is a major part of the puzzle when it comes to online marketing, advertising, and sales. As such, consumer data has become one of the most valuable resources that an organization can obtain. However, collecting consumer data is really only one half of the equation. Making use of it, is a whole different story.
In fact, utilizing consumer data in day-to-day operations is a pursuit that has created an entire industry of technology and software dedicated to just that. This all started back in the 1970s with ETL technology aimed at breaking down the data-silos that would inevitably develop within an organization’s infrastructure. However, centralizing the data in a single space gave rise to a slew of other issues.
In turn, yet another technology was invented in order to make the now centralized data more accessible and actually operational. This, creatively, was given the name reverse ETL. As the name suggests, this software functions as the reverse of ETL. Rather than centralizing data in a warehouse, reverse ETL is aimed at distributing consumer insights from the warehouse to other SaaS platforms being used for growth, sales, marketing, and development. This is all part of the modern data stack.
However, another key element to the modern data stack is the DBT Cloud.
The DBT Cloud is a cloud-based program that allows data-engineers to bring the best practices of data-analytics into real-time queries. DBT stands for data build tool. This is a feature that is absolutely critical to include in any modern data stack.
One of the elements of the DBT Cloud is the IDE or integrated development environment. This feature alone saves both time, and money in the development process. In the integrated development environment, data engineers can perform live query checks on their various models. This way, they are able to make sure that their queries and models are functioning properly along the entire development process.
Without the IDE, developers would have to continually copy their work from a text based document into the query-checker, and have to run the entire model. This is simply an inefficient development process. Not to mention, this makes it much more difficult to find the errors in complex models. Compared to the ability of the IDE, a developer can run checks as they go with the press of a button, and incrementally save their progress to identify errors when they occur.
The DBT Cloud isn’t just helpful in the context of developing queries and data models against the data in the warehouse, but it also brings easy, simple, and smooth orchestration functionality to the fingertips of data engineers and other users.
While in the development phase, models are run manually, but that doesn’t work when scaled. DBT Cloud makes scheduling your models to run and update regularly as easy as can be. This way, everyone relying on fresh data has it exactly when they need it.
Continuous integration is another integral aspect of DBT Cloud that makes it well worthwhile. However, it can be an arduous process to develop models with continuous integration when the entire model has to be tested with every single update. No matter how minor. This is why DBT Cloud came out with a Slim CI feature.
The Slim CI feature allows users to develop models and add them incrementally to ongoing development projects. Then, rather than testing the entire model, the Slim CI only tests the additional models being added.
This saves time, money, and a whole lot of stress.
Data is only projected to continue climbing in value. As such, the organizations and brands that implement systems and processes to optimize and operationalize their collected data will have a competitive advantage over those that don’t. Bringing the DBT Cloud into your modern data stack will help your developers create functional data models, programs, and queries that inform and empower the entire operation as a whole.