Data Build Tool (dbt) Data Integration Platform

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A Data Build Tool (dbt) Data Integration Platform is an data integration platform.



References

2023

  • chat
    • DBT stands for Data Build Tool, it is an open-source tool that enables Data Engineers to transform, test, and deploy data pipelines in a more organized and efficient way.

      DBT can be used in a data warehouse environment to apply transformations to raw data, which is then used for analysis and reporting purposes. It provides a modular approach to building data pipelines, where data is transformed and loaded into tables that can be used by downstream applications. DBT helps Data Engineers to manage complex data transformations, version control, and testing in a more organized way.

    • Some of the key features of DBT are:
      • Modularity: DBT allows Data Engineers to break down complex data transformations into smaller, more manageable chunks.
      • Testing: DBT provides a testing framework that enables Data Engineers to ensure data quality and accuracy.
      • Documentation: DBT automatically generates documentation for data pipelines, making it easier for Data Engineers to understand how data is transformed and loaded into tables.
      • Version Control: DBT integrates with version control systems such as Git, enabling Data Engineers to manage changes to data pipelines over time.

2021

  • https://www.datameer.com/blog/a-review-of-dbt-and-the-top-missing-features/
    • QUOTE: Dbt has become a market darling for data transformation tools – the T in your ELT data stack and processes. The simplistic approach of dbt – SQL coding – is both its biggest strength and weakness. I have for you a review of Dbt and the top missing features.

      We took a detailed look at dbt examining how it works, exploring how companies use it, and its overall ability to solve the data transformation problem. We published a more comprehensive review of dbt here and compared it to our offering, Datameer.

      At the very simplest level, dbt is really an Interactive Development Environment (IDE) for SQL data modeling. Just one look at the dbt UI, and you’ll see an IDE, with items arranged into projects (folders) and workspaces where you write your code. And almost every operation you perform outside of coding is done via their Command Line Interface (CLI), just like in your typical IDE. Integration with GitHub also adds to the IDE look-and-feel.

      Even though dbt claims to try to deliver the task of “analytics engineering” to everyday data analysts, the need for deep SQL expertise and mild to average Python skills to use the Jinja templating language really prevents dbt from reaching this goal. The company articulates that vision for a coding-centric approach in this quote from their website: ...

2021

  • https://www.datameer.com/blog/a-review-of-dbt-and-the-top-missing-features/
    • QUOTE: ... Dbt has become a market darling for data transformation tools – the T in your ELT data stack and processes. The simplistic approach of dbt – SQL coding – is both its biggest strength and weakness. I have for you a review of Dbt and the top missing features.

      We took a detailed look at dbt examining how it works, exploring how companies use it, and its overall ability to solve the data transformation problem. We published a more comprehensive review of dbt here and compared it to our offering, Datameer.

      At the very simplest level, dbt is really an Interactive Development Environment (IDE) for SQL data modeling. Just one look at the dbt UI, and you’ll see an IDE, with items arranged into projects (folders) and workspaces where you write your code. And almost every operation you perform outside of coding is done via their Command Line Interface (CLI), just like in your typical IDE. Integration with GitHub also adds to the IDE look-and-feel. ...