DBT vs Databricks

Welcome to the DBT vs. Databricks blog. A data build Tool (DBT) is a widely used platform for data transformation, schema management, data modeling, and other related tasks. At the same time, Databricks is a popular enterprise-grade open-source analytics framework for data engineering and data science.

Databricks and DBT are the two widely used data engineering frameworks that play a significant role in quickly turning raw data into usable information and providing a collaborative environment for data engineering teams. Databricks offers a unified environment for seamless data analytics, integrations, and data science, whereas DBT helps organizations build trusted data sources for operation workflows, ML, data modeling, etc.

Though these two frameworks play a significant role in data engineering, they are entirely different. Let's compare DBT and Databricks.

What is a Data Build Tool

DBT is an open-source platform that transforms data in a cloud data warehouse. It allows data engineers to use simple SQL statements to define data models and create SQL code that simplifies data analysis to perform data transformation operations in the data warehouse.

DBT is available in two different versions: DBT cloud and DBT on-prem. DBT offers the most reliable features, such as handling boilerplate code, code compiler, project DBT documentation, DBT tests, package manager, data snapshots, etc.

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What is Databricks?

Databricks is an open-source, scalable data analytics solution that supports data engineering teams in building, deploying, sharing, and maintaining enterprise-grade analytics. 

Databricks offers a wide range of tools that enable data engineers to connect with source systems and perform operations like data processing, sharing, modeling, and analyzing data. It also offers an intuitive UI using which you can perform ETL tasks, create dashboards, manage security, and perform ML operations. 

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Databricks vs DBT


Parameter Data Build Tool (DBT) Databricks
Application The applications of data build tools include data transformation,  data warehouse, analytics, data modeling, schema management, BI reporting, etc. Databricks are widely used in data streaming, ETL workflows, Big Data processing, AI, ML, data science, and predictive analytics. 
Data Processing This platform is primarily designed for data transformation operations and does not support any sort of data processing operations. Databricks supports data processing types such as batch processing, machine learning data processing, stream data processing, etc.
Machine learning  DBT doesn't come with any machine learning capabilities but it can be integrated with  ML libraries.  Databricks is a versatile framework and provides a scalable environment for building models.
Data Transformation DBT allows users to write simple SQL statements to perform any data transformation. In addition to SQL, we can leverage Python to perform data operations. Databricks allows data transformation by leveraging Apache Spark's APIs and libraries 
Query Language The primary language is SQL, and it also supports Python Databricks supports query languages such as Python, R, Scala and SQL.
Deployment DBT is available in two products and can be deployed as an on-prem solution and cloud solution. Databricks is a cloud-based framework that can be deployed on any popular cloud platform, such as Azur, AWS, and GCP. 
Security The data build tool offers secured access to data sets and data and connection encryption features. Moreover, it provides role-based access control features.  Databricks prioritizes security and offers built-in features to integrate Active Directory, encryption, network isolation, and more.
Integration DBT seamlessly integrates with all popular cloud data warehouse platforms and storage systems. Databricks is a highly flexible platform offering built-in connectors for various platforms and data sources.
Performance  DBT leverages caching and parallelism to deliver high performance. Datarbciks makes use of Apache spark capabilities to deliver high performance.
Scalability Scalable platform and supports enterprise grade projects and teams. Databricks is a scalable platform that can scale up and down per organizational requirements. 
Monitoring DBT offers log management and error handling to monitor various data transformation operations. Databricks offers workspace feature to monitor 
Pricing Pay-as-you-go pricing Pay-as-you-go model

Data Build Tool Features:

Following are some of the popular features offered by DBT, and they play a key role in performing data transformation operations:

  • Data Transformation
  • Data Validation
  • Data Integration
  • Automation
  • Data Versioning
  • Customizations & Scalability
  • Dependency Management
  • Workflow Orchestration
  • Multi-data format support
  • Data Quality Management
  • Easy Collaboration
  • Effective Documentation 

Related Article: DBT Interview Questions

Databricks Features:

The following are the Popular features offered by databricks:

  • Unified Analytics Platform
  • Distributed Computing
  • Collaborative Workspace
  • Managed Spark Clusters
  • Integrations
  • Built-in machine learning workflows,
  • data visualization capabilities
  • Real-time data processing
  • Auto Scaling
  • APIs and SDKs

Related Article: Azure Data Factory Vs Databricks


DBT and databricks belong to the data engineering segment and help data engineers, analysts, and data scientists simplify organizational data analytics. DBT has become a first choice for organizations to perform operations such as data transformation, data modeling, etc. Databricks is a comprehensive framework for handling enterprise-grade analytics that leverages Apache spark power and supports AI and ML operations. 

By Tech Solidity

Last updated on May 22, 2024