Recent rise of modern technologies like cloud computing, big data platforms (e.g., Hadoop, Snowflake), and real-time data processing tools (e.g., Apache Kafka) has led to the emergence of new practices in data processing. As businesses started generating big amounts of data and needed it processed efficiently, traditional IT roles like database administrators weren't enough. This led to the creation of data engineering as a specialized field to handle the complexities of modern data pipelines.

The need for scalable, flexible data solutions, combined with the growth of data science and the demand for real-time analytics, has driven the rapid development of data engineering. The role itself only gained recognition within the last decade, and educational programs focused on data engineering are still emerging. Continuous advancements in technology keep the field evolving, making it relatively new and dynamic.

Data engineering is the process of designing, building, and managing systems that collect, store, and process large amounts of data. It focuses on creating pipelines that transform raw data into usable formats for analysis, ensuring that data is reliable, accessible, and ready for use in data science, analytics, and business applications.

New approaches in data transforation gave birth to numerous tools which helps to reshap data from a raw mess to final data product.

Some of these tools are:

• Apache Spark
• dbt (Data Build Tool)
• Apache Beam
• Apache Flink
• Airbyte

Today we will focus on dbt. dbt (Data Build Tool) is an open-source command-line tool that enables data analysts and engineers to transform raw data in their warehouse more effectively. dbt helps create a version-controlled, automated process for transforming data from raw ingestion to clean, analytics-ready tables. It is designed to work primarily with SQL and leverages the power of modern data warehouses such as Snowflake, BigQuery, Redshift, and others.

Unlike traditional ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) tools, dbt focuses on the transformation phase of the data pipeline. dbt assumes that data is already loaded into your warehouse and focuses on transforming it from its raw form into a usable format for analysis.

dbt uses SQL as language for data transformation, making it accessible to analysts and engineers who are comfortable with SQL. Users define SQL queries as models, which are then compiled and executed in the data warehouse. Users create “models,” which are SQL queries that represent a particular transformation step. These models can depend on each other, and dbt automatically handles dependency resolution and execution order.

dbt has perfect integrations with Git, allowing teams to version control their transformations, collaborate effectively, and follow software engineering best practices such as code reviews and continuous integration (CI). This enables data transformations to be treated as code.

Documentation and testing are built-in features of dbt. It makes it easy to document models and their relationships, as well as to test data transformations. Users can write tests to validate assumptions about your data (e.g., checking for null values or ensuring uniqueness) and run these tests as part of your CI pipeline.

dbt uses Jinja, a popular in python programming language ecosystem templating engine. Templating helps to write more dynamic and reusable SQL code. This lets you write SQL code that can be parameterized, enabling logic like conditional statements or looping, which would otherwise be difficult to express in SQL.

dbt has tools to be run locally or as part of a scheduled job (e.g., using Airflow, dbt Cloud, or other orchestration tools). This enables you to automate your data transformations and ensures that your data models stay up to date.

dbt supports different environments (e.g., development, staging, production), allowing users to safely develop and test their transformations before deploying them to production. It also supports multiple profiles for connecting to different data warehouses or environments (e.g. PostgreSQL databases, AWS, GCP).

Key features of dbt that make it easy-to-learn tools from my point of view:

• SQL Modeling: dbt’s core functionality is to build models, which are SQL files that define transformations. dbt uses DML (data manipulation language) subset of SQL, making unnecessary to write complex CREATE TABLE or CREATE VIEW statements. These models can be chained together to create complex workflows.

• Testing: Users can define tests to validate data models, ensuring data quality as transformations run.

• Documentation: dbt automatically generates documentation for your models, including dependency graphs, making it easier to understand and maintain your transformations.

• Jinja Macros: dbt’s use of Jinja templating enables code reuse and dynamic SQL generation, which simplifies writing complex queries. Jinja is well known engine in python world. So, for python engineers like me it is easy to start to use it inside dbt.

• Incremental Models: dbt supports incremental transformations, allowing you to process only new or changed data, which is more efficient for large datasets. It can be very beneficial in cloud environments where computing time can be expensive.

dbt exist in two "versions": dbt Cloud and dbt Core

• dbt Core is the open-source command line tool that you can run locally or in your own cloud environment. It can be installed as usual python package into virtual environment or in docker container. Users can also install the dbt-core package through a data warehouse specific package like dbt-snowflake that contains all the code needed for dbt to interact with the data warehouse.

• dbt Cloud: A managed service that provides a web-based interface for managing and deploying dbt projects. It includes additional features like job scheduling, logging, and team collaboration tools. In fact dbt Cloud is a cloud-based integrated development environment (IDE) which is built on top of dbt Core. It allows not only to write and run code (SQL, Python, and Jinja). You can visualize model relations in a DAG, generate docs, and version control code changes via git. The editor even has ergonomic features like code diff view, autocomplete, and a code formatter.

dbt Cloud is proprietary software for teams, but for single developers in has Free plan. Paid plans provide more out of the box. This includes a browser-based IDE for development, job scheduling, job logging, monitoring and alerting, job documentation, version-control integration, single sign-on, role-based access controls and access to an API.

Main benefits of using dbt:

• dbt enables analysts to write transformations in SQL without relying on data engineers for complex ETL processes. But it still requires at least basic knowledge of SQL.
• dbt’s testing framework helps ensure that data models are reliable and that assumptions about the data are verified. It increases overall quality of data pipelines.
• dbt scales well with cloud data warehouses, allowing teams to process large datasets efficiently.
• dbt has integration with most of popular data warehousing solutions like Amazon Redshift, BigQuery, Snowflake. dbt has plug-in system that allows users to write their data adapters and packages.

As every tool dbt has some limitattions:

• dbt is not ETL/ELT tool. It does not handle data extraction and loading, so you still need other tools for these parts of the pipeline.
• dbt is focused on SQL transformations, which may not be ideal for complex transformations that require other languages like Python or Java.
• dbt is suitable SQL-based warehouses, it does not work with no-SQL databases or object storages.

dbt (Data Build Tool) is a powerful tool for transforming data within cloud data warehouses, enabling teams to adopt software engineering best practices in their data pipelines. Its focus on modular, SQL-based transformations, combined with features like testing, documentation, and version control, makes it a popular choice for analytics engineers and data teams aiming to build reliable, scalable data models.