Welcome to Data Build Tool Interview Questions! The DBT has gained massive traction in the data engineering and analysis segment with commendable features. It is an open-source platform to help data engineers transform, model, and analyze data.
The demand for skilled DBT professionals is rising, and it is vital to understand concepts that are asked in any DBT interview. In this blog, we will discuss all the essential questions of DBT, along with detailed answers. Without wasting too much time, let's start our preparation.
The Data build tool is an open-source platform designed to transform data warehouse data with SQL select statements. Using DBT, you can perform tasks like data transformation, modeling, testing & validation, etc. DBT simplifies the data engineer's tasks and prepares the data for analysis and reporting.
DBT is widely used for performing the following tasks:
Most ETL platforms perform data transformation outside the data warehouse, whereas DBT performs data transformations within the data warehouse and is highly suitable for ELT workflows. Unlike other ETL tools, it offers version control and validation for data models.
Jinja is a templating language, and in DBT, we combine SQL with Jinja. Jinja offers a programmatic environment with more capabilities that are impossible alone with SQL.
In DBT, Jinja will allow developers to:
Following are the custom Jinja blocks:
To run DBT commands, you can select a path.
It should be done at the directory level when performing bulk configurations, which is far simpler.
A DBT project is a directory consisting of all the components needed to deal with data within DBT. A typical DBT project contains ingredients like project name, YAML configuration file, data sources & destinations, transformation requirements, SQL queries, SQL templates, Snapshots, etc.
YAML stands for Yet Another Markup Language, a popular data serialization language. It is used in DBT to write configuration files. YAML is mandatory for every DBT project.
In DBT, a model is a logic used for data transformation or sometimes an intermediate step in the data transformation process. When you hit DBT “run,” you are running a model to transform data as per the specifications.
Generally, developers spend their time working with models that can be modified and scalable to maximize efficiency and meet growing requirements.
Snapshots are one of the core features available in dbt that help access historical data. This feature is handy while working with mutable tables in DBT and provides a Snapshot mechanism. DBT Snapshot records changes that occurred to a mutable table over some time.
Seeds are nothing but CSV files that are stored in a DBT project. The seed files can be loaded to a data warehouse using the dbt seed command.
The DBT Test command helps you test your sources, seeds, and snapshots and give you the results.
Macros are part of Jinja and are very helpful in eliminating the need to write the same code across DBT models. Macros are a code block that can be reusable across multiple projects and stored as a .sql file in the macros directory.
The Job Scheduler is the critical component for running DBT jobs in the cloud and simplifies the process of building data pipelines. It can handle both event-based and CORN-based schedules.
Following are the DBT API’s:
Following are the ways you can use to access DBT APIs:
The DBT cloud CLI allows developers to write code from a local command line interface or code environment. In contrast, DBT cloud IDE allows users to develop DBT projects directly in the browser.
IN DBT, you will get the option called logs and diagnostics, which helps you identify and troubleshoot issues.
By Tech Solidity
Last updated on February 12, 2024