What Is DBT and Why You Should Use It?

Data storage, conversion, and synchronization have remained largely the same for most companies across the board since 2010. However, there is a better solution allowing data engineers, data analysts, and executives to have greater control and usability through implementing the data build tool or DBT.

Rather than requiring expensive data storage methods or needing an intensive knowledge of datasets only accessible to specific individuals with specialized backgrounds and expertise, DBT can provide a comprehensive, user-friendly solution for everyone for transforming extracted data from data warehouses.

Problems Faced with ETL for Data Analyst and Engineers

The ETL (Extract, Transform, and Load) pipeline is a process that converts data from an input source and then sends it to a data warehouse for analysis and other critical data processes such as synchronization. During the extraction process, the data is collected and then moved to the second step which transforms it into a format that is usable by many applications. There are several issues data engineers face when managing the ETL, however, and can include the following.

• Loss of data
• Issues with software requirements
• Trouble with test build data
• Business flow information can be unreliable
• SQL coding skills are necessary

These issues are headaches for engineers and analysts alike because they make getting the necessary data and conversion unreliable and time-consuming. Data sets can be complex to manage and generate information consumers may not trust. In contrast, the DBT eliminates many of the notorious issues engineers face when working with the ETL pipeline, including locating and sensing test issues.

What Is DBT?

This command-line tool allows the user to transform data in the ETL process effectively. The information is extracted and then sent to the DBT to be transformed into usable, more reliable, and easier use. It eliminates many of the special and complex skills and glitches that plagued the old process.

In fact, DBT is so user-friendly, and you don't need to be an engineer or an analyst to use it, which is a prime example of why data engineers should use DBT. This feature means other company members can easily collaborate with both data engineers and analysts to make business decisions, which can eliminate confusion or problems sharing accurate data across all departments of a company.

Also, the DBT makes locating test problems in the system automatic and flags problems, making them easier to identify and diagnose. However, ETL has been essential for a business learning how to gather and convert raw data to make better decisions and move forward with business processes. Still, it has been exceedingly complex and filled with issues until the creation of DBT.

Why Use DBT for Your Company?

Incorporating DBT means professionals who struggled before with processing raw data from the data warehouse and through the ETL pipeline can reduce their workloads and convert easily readable data for many applications. This is a big part of why data engineers should use DBT. In addition, this tool has built-in testing, which vastly improves data quality and reliability, which was a struggle in the past.

Learning how to use DBT is incredibly simple as previously mentioned, and many others within a company can easily learn how to use it effectively. This means executives can now play an integral part in the ETL process and share information and ideas across departments with ease. You don't need special skills or coding knowledge to operate the DBT tool, making it incredibly versatile and flexible.

You may also be wondering is DBT for data analyst looking for a better way to collect and model data for various applications and projects? Another benefit to using DBT is that it features a searchable catalog of data to access and locate information data hassle-free. It's a tremendous asset to find information quickly for non-engineers and analysts, creating and implementing data fast and reliable and eases the workload on engineers who may feel pressure to produce usable data from complex data sets.

Standout Features of DBT

A few features of DBT that genuinely stand out and make it a must for companies looking to streamline and simplify data collection, conversion, and storage across departments. One of the most notable is version control. As a project is in construction and collaboration, the versions can be submitted for review and revised within the tool with ease.

DBT also allows users to build models that can be modified and understood easily using direct acyclic graphs or DBTs. This feature means models can be created and modified or even debugged without complications.

Environment management capabilities also allow the user to readily identify data and maintain it for their project without changing it or wonder where the data was generated or which project the data is coming from internally. It allows for the separation of information that is reliable and accurate for testing.

Perhaps one of the latest updates and most valuable aspects of DBT is the ability to deploy information to the cloud for storage. This feature allows for great automation and storage capacity without a complex system for managing converted data. It can automatically be generated and uploaded to the cloud automatically, thus reducing issues and ensuring its integrity and safety.

DBT can also generate necessary documentation using many sources of information, including descriptions generated from the .yml files, sources, model dependencies, table stats, and more, making the information reliable and multi-faceted.

Tips for Deploying a DBT

There are a few things to consider when deploying a DBT to ensure they are used as effectively as possible, and those who access the tool can be on the same page. One of the first tips to consider is ensuring all team members involved agree on how the tool should be used within the framework.

Another useful tip is to use production environments separately from development environments, easily accomplished with the DBT. It helps to know that ff there are several DBT users writing code, code use a style guide and code conventions.

Another tip is to avoid using or referencing raw data for clarity. This data should only be used in a single reference point. It is also helpful to rename tables and fields to fit conventions and recast those fields into the appropriate data type. 

Incorporating DBT Into Your Business Model

So, why use DBT? Because there are currently over 2000 companies using DBT successfully within their company. They have noticed a boost in production and usable information driving essential company decisions. Each department can access valuable information that is easy to use and understand, making solutions and projects easier to accomplish and more reliable.

The DBT allows you to reduce workloads for data engineers and makes jobs easier for data analysts because they have quick access to accurate information that can be modified to create models and incorporate into projects and reports without data uncertainties and issues with how the data was generated.

In short, incorporating the DBT into your business data collection and storage model has many valuable benefits. It makes the entire process more manageable and less confusing, and reliable for all involved. In fact, allows others to be involved where it may not have been possible before. If you haven't considered using the DBT, now is time to weigh your options and consider the vast benefits you can harness by eliminating the old school methods and updating to a versatile and highly user-friendly approach.

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