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  1. Stackups
  2. Application & Data
  3. Databases
  4. Database Tools
  5. AzureDataStudio vs Dataform

AzureDataStudio vs Dataform

OverviewDecisionsComparisonAlternatives

Overview

AzureDataStudio
AzureDataStudio
Stacks89
Followers108
Votes0
GitHub Stars7.7K
Forks961
Dataform
Dataform
Stacks818
Followers53
Votes0
GitHub Stars934
Forks188

AzureDataStudio vs Dataform: What are the differences?

Introduction

In this article, we will explore the key differences between Azure Data Studio and Dataform.

  1. Integration with Microsoft Azure: Azure Data Studio is a lightweight cross-platform database tool that is specifically designed to provide a modern, productive SQL server editing experience. It is tightly integrated with Microsoft Azure services, allowing users to easily manage their Azure SQL databases, Azure Cosmos DB, and Azure Data Warehouse. Dataform, on the other hand, is an open-source tool designed to manage the full lifecycle of SQL data warehouses, including data transformation, testing, and documentation. While it can be used with Azure SQL databases, it is not primarily focused on Azure integration.

  2. Code Editor Functionality: While both Azure Data Studio and Dataform provide code editing capabilities, Azure Data Studio offers a richer set of features. It includes advanced query execution plans, query performance insights, IntelliSense, and customizable keyboard shortcuts. Additionally, Azure Data Studio supports multiple languages such as Transact-SQL, Python, and PowerShell, making it a versatile tool for data professionals. Dataform, on the other hand, focuses more on the declarative definition of data transformation workflows, providing a simplified coding experience with a specific focus on SQL data warehouses.

  3. Collaboration and Version Control: Azure Data Studio provides built-in version control integration with Git, allowing teams to collaborate and track changes to their SQL scripts. It also supports collaborative workspaces where multiple users can share and edit scripts in real-time. Dataform, on the other hand, offers more advanced collaboration features with its integration with Git and workflows designed for large-scale data teams. It includes features like code reviews, pull requests, and automated testing, making it suitable for teams working on complex data transformation projects.

  4. Data Orchestration and Automation: Dataform specializes in the orchestration and automation of data workflows. It provides a declarative way to define data transformations, allowing users to create complex dependencies and schedules for their SQL scripts. Additionally, Dataform offers features like incremental builds and caching, enabling faster data transformation cycles. Azure Data Studio, on the other hand, does not have built-in support for data orchestration and automation, as it is primarily focused on providing a SQL editing and management experience.

  5. Data Testing and Documentation: Dataform places a strong emphasis on data testing and documentation. It provides built-in features to write and execute tests on SQL scripts, ensuring the quality and integrity of data transformations. Dataform also generates documentation automatically based on the metadata defined in the SQL scripts. Azure Data Studio, although it supports the execution of SQL scripts, does not have built-in features for data testing and documentation.

  6. Extensibility and Community Support: Azure Data Studio has a rich ecosystem of extensions, allowing users to customize their development environment and leverage additional functionalities. It also has an active community that contributes to the development of new extensions and features. Dataform, being an open-source tool, also benefits from community contributions, but it has a more limited set of extensions and community support compared to Azure Data Studio.

In summary, Azure Data Studio is primarily focused on providing a rich SQL editing and management experience with strong integration with Microsoft Azure services, while Dataform specializes in the full lifecycle management of SQL data warehouses, with a particular emphasis on data transformation, testing, and documentation.

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Advice on AzureDataStudio, Dataform

Manikandan
Manikandan

Software Engineer at Blitzscaletech Software Solution

Jul 20, 2020

Needs adviceonDBeaverDBeaverAzureDataStudioAzureDataStudio

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

AzureDataStudio
AzureDataStudio
Dataform
Dataform

It is a cross-platform database tool for data professionals using the Microsoft family of on-premises and cloud data platforms on Windows, MacOS, and Linux.

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

Cross-Platform DB management for Windows, macOS and Linux with simple XCopy deployment; SQL Server Connection Management with Connection Dialog, Server Groups, Azure Integration and Registered Servers; Object Explorer supporting schema browsing and contextual command execution; T-SQL Query Editor with advanced coding features such as autosuggestions, error diagnostics, tooltips, formatting and peek definition; Query Results Viewer with advanced data grid supporting large result sets, export to JSON\CSV\Excel, query plan and charting; Management Dashboard supporting customizable widgets with drill-through actionable insights; Visual Data Editor that enables direct row insertion, update and deletion into tables
Version ontrol; Scheduling; Notifications and logging; Assertions; Web based development environment; Alerting; Incremental tables; Packages; Reusable code snippets; Unit tests; Data tests
Statistics
GitHub Stars
7.7K
GitHub Stars
934
GitHub Forks
961
GitHub Forks
188
Stacks
89
Stacks
818
Followers
108
Followers
53
Votes
0
Votes
0
Integrations
Linux
Linux
Git
Git
macOS
macOS
Microsoft SQL Server
Microsoft SQL Server
Windows
Windows
Azure SQL Database
Azure SQL Database
Amazon Redshift
Amazon Redshift
Google BigQuery
Google BigQuery
GitHub
GitHub
JavaScript
JavaScript
PostgreSQL
PostgreSQL
Snowflake
Snowflake
Git
Git

What are some alternatives to AzureDataStudio, Dataform?

dbForge Studio for MySQL

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

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

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.

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

dbForge Studio for SQL Server

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

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

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

DBeaver

DBeaver

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.

dbForge SQL Complete

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

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.

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