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Dataform vs dbt: What are the differences?
Dataform: A framework for managing SQL based data operations *. It 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; *dbt:** A command line tool that enables data analysts and engineers to transform data in their warehouse more effectively. dbt - Documentation.
Dataform and dbt can be categorized as "Database" tools.
Dataform is an open source tool with 77 GitHub stars and 7 GitHub forks. Here's a link to Dataform's open source repository on GitHub.
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Learn MorePros of Dataform
Pros of dbt
Pros of Dataform
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Pros of dbt
- Easy for SQL programmers to learn3
- CI/CD2
- Schedule Jobs2
- Reusable Macro2
- Faster Integrated Testing2
- Modularity, portability, CI/CD, and documentation2
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Cons of Dataform
Cons of dbt
Cons of Dataform
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Cons of dbt
- Only limited to SQL1
- Cant do complex iterations , list comprehensions etc .1
- People will have have only sql skill set at the end1
- Very bad for people from learning perspective1
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- No public GitHub repository available -
What is Dataform?
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.
What is dbt?
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.
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What companies use Dataform?
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What are some alternatives to Dataform and dbt?
Slick
It is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred.
NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
Tableau
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
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.