Modern organizations are largely depending on continuously generating data to make valid business decisions. Over the decade we have seen great technological advancements in the data processing segment. Along with the technologies, the data growth rate has spiked to a massive extent and there is a huge need for centralized data-sharing platforms to make the data available for all the decision-makers.
Raw Data has to travel through advanced technologies such as ETL, data warehouse platforms, databases, BI tools, etc. The data build tool fits at the data transformation level and is specifically designed to build, test, deploy, and document organizational data infrastructure. Let's have a detailed understanding of the Data build tool.
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Data Build Tool is an Open Source software platform that streamlines the process for data engineers, data scientists, and BI professionals by making data transformation more simple and reliable. It allows users to transform data in a data warehouse by writing simple SQL statements.
Moreover, you can use DBT to perform core functions such as writing business logic, deploying code, automating data quality tests, tracking lineage, documenting code, and more. Users can define data models using SQL code and this code can be run on top of any data warehouse or storage system. DBT supports organizations in building scalable and easy-to-maintain data infrastructure.
Want to learn end-to-end implementation of DBT from experienced data engineering architects? Check out this Data Build Tool Training Mastery Program"
The data build tool is a highly scalable framework that can fit into any cloud storage and data warehouse system. Implementation of DBT varies from organization to organization but the following are some of the common data engineering areas where DBT is being used.
1) To Build & Manage Data Pipelines
DBT allows you to build & Optimize SQL-based models that run on any cloud data warehouse or storage systems. DBT eliminates data barriers and allows organizations to build a data infrastructure that is easy to scale.
2) To Test Data Quality & Integrity
It is very essential to ensure that the data we are using is accurate and usable for further data processing operations. DBT comes with built-in features to test data quality and integrity. You can perform actions such as track lineage, run data validation tests, etc.
3) To Enhances Data Consistency & Transformation Process
Using DBT features, organizations can build a standardized approach that ensures data quality, consistency, and integrity. It not only eliminates the struggle for data engineers but also gives confidence to organizations to gain insights and make business decisions.
4) Team Collaboration
Collaboration of teams is very essential to have good coordination between different data engineering teams. DBT offers a collaborative environment where different teams can collaborate and share the same datasets and models to build analytics and reports. Moreover, collaboration becomes more helpful while working on complex data engineering projects.
DBT offers the following two products:
Let's discuss each product:
DBT Core is a command-line open-source tool that helps data teams define and build data models using a structured query language (SQL). And then we can generate optimized SQL code that is highly compatible and run on any data storage or data warehouse system.
DBT cloud offers advanced features & functionality compared to DBT core. It consists of a graphical interface using which it becomes seamless to manage data models, scheduling, integrations, collaborations, scheduling, and many other operations.
Following are some of the common tasks performed using DBT:
To learn DBT you need to have basic knowledge of SQL, GIT, and Data modeling
Let's understand how each of these prerequisites is essential to learning DBT:
SQL: DBT uses SQL as its core programming language to run select statements. So, you need to have a clear understanding of SQL.
GIT: Often we use Git while working with DBT. you need basic knowledge of Git to get started with DBT.
Modeling: Data modeling is one of the core aspects of any ETL process and it helps you build clear data structures.
We are offering an end-to-end DBT learning program where you can learn things from scratch including Git and modeling.
By now you may have got a clear idea of what DBT is and how its powerful features make the data transformation process far easier compared to other tools in the market. DBT Cloud has become more popular because of the flexibility it offers, its simple user interface, and works easily with top cloud data warehouse platforms like Snowflake, redshift, azure synapse, etc.
By Tech Solidity
Last updated on April 6, 2023