DBeaver vs dbt

Overview

DBeaver

Stacks555

Followers731

Votes67

GitHub Stars46.0K

Forks3.9K

dbt

Stacks517

Followers461

Votes16

DBeaver vs dbt: What are the differences?

Key Differences between DBeaver and dbt

DBeaver and dbt are both powerful tools used in data management, however, they have some key differences that set them apart from each other.

Data Exploration vs Transformation: DBeaver is primarily a data exploration tool that allows users to interact with and analyze data stored in various databases. On the other hand, dbt (data build tool) is designed for data transformation, providing a framework and infrastructure to transform raw data into a structured and reliable format for analysis.
User Interface vs Command Line Interface: DBeaver offers a user-friendly graphical user interface (GUI) that allows users to visually navigate, query, and manipulate data. In contrast, dbt operates through a command line interface (CLI), utilizing a series of commands and configurations to execute data transformation tasks.
Database Support: DBeaver supports a wide range of databases including MySQL, PostgreSQL, Oracle, SQL Server, and more. It provides a consistent interface to work with different database systems. dbt, on the other hand, is tightly integrated with the Snowflake data warehouse and Redshift, providing specific functionality tailored for these platforms.
Focus on SQL vs Compilation and Testing: DBeaver heavily relies on SQL queries to interact with and manipulate data, providing a seamless SQL development experience. In contrast, dbt places a strong emphasis on compilation and testing of data models, ensuring the accuracy and consistency of transformed data by validating the SQL code against defined rules and expectations.
Version Control and Collaboration: DBeaver lacks built-in version control and collaboration capabilities, making it challenging for teams to work together efficiently and manage changes to the database schema. dbt, however, integrates well with popular version control systems like Git, enabling collaborative development and easy tracking of changes to data models.
Community and Ecosystem: DBeaver has a large and active community, providing a wealth of resources, plugins, and extensions, making it a versatile and flexible tool. dbt, on the other hand, has a smaller yet growing community that is focused on data engineering best practices. The dbt community provides a curated collection of onboarding materials, lessons, and templates to facilitate the adoption and usage of dbt.

In summary, DBeaver is a versatile data exploration tool with a wide database support and user-friendly interface, while dbt is a powerful data transformation tool that focuses on compilation, testing, version control, and community-driven best practices for building reliable data pipelines.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on DBeaver, dbt

Manikandan

Software Engineer at Blitzscaletech Software Solution

Jul 20, 2020

Needs adviceon

DBeaver

AzureDataStudio

Which tools are preferred if I choose to work on more data side? Which one is good if I decide to work on web development? I'm using DBeaver and am now considering a move to AzureDataStudio to break the monotony while working. I would like to hear your opinion. Which one are you using, and what are the things you are missing in dbeaver or data studio.

1.74M views1.74M

Comments

Detailed Comparison

DBeaver	dbt
It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc.	dbt is a transformation workflow that lets teams deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.
-	Code compiler; Package management; Seed file loader; Data snapshots; Understand raw data sources; Tests; Documentation; CI/CD
Statistics
GitHub Stars 46.0K	GitHub Stars -
GitHub Forks 3.9K	GitHub Forks -
Stacks 555	Stacks 517
Followers 731	Followers 461
Votes 67	Votes 16
Pros & Cons
Pros 22 Free 13 Platform independent 9 Automatic driver download 7 Import-Export Data 6 Simple to use	Pros 5 Easy for SQL programmers to learn 3 Reusable Macro 2 Faster Integrated Testing 2 Modularity, portability, CI/CD, and documentation 2 CI/CD Cons 1 Very bad for people from learning perspective 1 People will have have only sql skill set at the end 1 Cant do complex iterations , list comprehensions etc . 1 Only limited to SQL
Integrations
Oracle PostgreSQL Linux MySQL Presto MariaDB Java Apache Hive Microsoft SQL Server macOS	Exasol Snowflake Materialize Presto Amazon Redshift Google BigQuery PostgreSQL Apache Spark Dremio Databricks

What are some alternatives to DBeaver, dbt?

dbForge Studio for MySQL

It is the universal MySQL and MariaDB client for database management, administration and development. With the help of this intelligent MySQL client the work with data and code has become easier and more convenient. This tool provides utilities to compare, synchronize, and backup MySQL databases with scheduling, and gives possibility to analyze and report MySQL tables data.

dbForge Studio for Oracle

It is a powerful integrated development environment (IDE) which helps Oracle SQL developers to increase PL/SQL coding speed, provides versatile data editing tools for managing in-database and external data.

dbForge Studio for PostgreSQL

It is a GUI tool for database development and management. The IDE for PostgreSQL allows users to create, develop, and execute queries, edit and adjust the code to their requirements in a convenient and user-friendly interface.

dbForge Studio for SQL Server

It is a powerful IDE for SQL Server management, administration, development, data reporting and analysis. The tool will help SQL developers to manage databases, version-control database changes in popular source control systems, speed up routine tasks, as well, as to make complex database changes.

Liquibase

Liquibase is th leading open-source tool for database schema change management. Liquibase helps teams track, version, and deploy database schema and logic changes so they can automate their database code process with their app code process.

Sequel Pro

Sequel Pro is a fast, easy-to-use Mac database management application for working with MySQL databases.

dbForge SQL Complete

It is an IntelliSense add-in for SQL Server Management Studio, designed to provide the fastest T-SQL query typing ever possible.

Knex.js

Knex.js is a "batteries included" SQL query builder for Postgres, MySQL, MariaDB, SQLite3, and Oracle designed to be flexible, portable, and fun to use. It features both traditional node style callbacks as well as a promise interface for cleaner async flow control, a stream interface, full featured query and schema builders, transaction support (with savepoints), connection pooling and standardized responses between different query clients and dialects.

Flyway

It lets you regain control of your database migrations with pleasure and plain sql. Solves only one problem and solves it well. It migrates your database, so you don't have to worry about it anymore.

PostGIS

PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.

Related Comparisons

DBeaver vs dbt: What are the differences?

Key Differences between DBeaver and dbt

DBeaver and dbt are both powerful tools used in data management, however, they have some key differences that set them apart from each other.

Data Exploration vs Transformation: DBeaver is primarily a data exploration tool that allows users to interact with and analyze data stored in various databases. On the other hand, dbt (data build tool) is designed for data transformation, providing a framework and infrastructure to transform raw data into a structured and reliable format for analysis.
User Interface vs Command Line Interface: DBeaver offers a user-friendly graphical user interface (GUI) that allows users to visually navigate, query, and manipulate data. In contrast, dbt operates through a command line interface (CLI), utilizing a series of commands and configurations to execute data transformation tasks.
Database Support: DBeaver supports a wide range of databases including MySQL, PostgreSQL, Oracle, SQL Server, and more. It provides a consistent interface to work with different database systems. dbt, on the other hand, is tightly integrated with the Snowflake data warehouse and Redshift, providing specific functionality tailored for these platforms.
Focus on SQL vs Compilation and Testing: DBeaver heavily relies on SQL queries to interact with and manipulate data, providing a seamless SQL development experience. In contrast, dbt places a strong emphasis on compilation and testing of data models, ensuring the accuracy and consistency of transformed data by validating the SQL code against defined rules and expectations.
Version Control and Collaboration: DBeaver lacks built-in version control and collaboration capabilities, making it challenging for teams to work together efficiently and manage changes to the database schema. dbt, however, integrates well with popular version control systems like Git, enabling collaborative development and easy tracking of changes to data models.
Community and Ecosystem: DBeaver has a large and active community, providing a wealth of resources, plugins, and extensions, making it a versatile and flexible tool. dbt, on the other hand, has a smaller yet growing community that is focused on data engineering best practices. The dbt community provides a curated collection of onboarding materials, lessons, and templates to facilitate the adoption and usage of dbt.

DBeaver vs dbt

Overview

DBeaver vs dbt: What are the differences?