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Data Build Tool - a tool to build data

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Manage episode 353388209 series 2522423
Contenuto fornito da Anthony Moendir & Tim Beeren, Anthony Moendir, and Tim Beeren. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Anthony Moendir & Tim Beeren, Anthony Moendir, and Tim Beeren o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
The guest of our show presented the Data Build Tool, DBT, in one of our knowledge-sharing sessions. Especially the fun part is what I recognize when I look at this knowledge-sharing session. The guest of the show presented it in an enthusiastic way and used terms like: the macro functionality makes DBT awesome and Ephemeral is the Unique Selling Point.
What this episode covers
In General

  • What is a Data Build Tool?
  • What problems does it solve?
  • What are the two versions? Cloud, CLI
  • Why is it much more interesting to use than other ETL, transformation tools?
  • Analytics Engineering
  • Explain a bit more in debt the Basics of a DBT project: dbt project data pipeline, data model, tests, macros.
    • Model dependencies
    • DBT-packages ecosystem
  • One of the powers of DBT is Jinja.
    • What could you compare Jinja to?
    • Is it easy to comprehend and learn?
  • Ephemeral (CTE) was the unique selling point for you, why?
  • An engineer enthusiastic about a documentation feature… that asks for an explanation.

Within bol.com

  • Usage started as an experiment, how are we using it in bol.com?
  • Can you share some learnings you and your team had when you started using DBT?

Guests
Sander Boumeester – Software Engineer in the experimentation team.


Notes
On the website of getdbt there's a cool picture that explains the framework really well.

  continue reading

135 episodi

Artwork
iconCondividi
 
Manage episode 353388209 series 2522423
Contenuto fornito da Anthony Moendir & Tim Beeren, Anthony Moendir, and Tim Beeren. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Anthony Moendir & Tim Beeren, Anthony Moendir, and Tim Beeren o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
The guest of our show presented the Data Build Tool, DBT, in one of our knowledge-sharing sessions. Especially the fun part is what I recognize when I look at this knowledge-sharing session. The guest of the show presented it in an enthusiastic way and used terms like: the macro functionality makes DBT awesome and Ephemeral is the Unique Selling Point.
What this episode covers
In General

  • What is a Data Build Tool?
  • What problems does it solve?
  • What are the two versions? Cloud, CLI
  • Why is it much more interesting to use than other ETL, transformation tools?
  • Analytics Engineering
  • Explain a bit more in debt the Basics of a DBT project: dbt project data pipeline, data model, tests, macros.
    • Model dependencies
    • DBT-packages ecosystem
  • One of the powers of DBT is Jinja.
    • What could you compare Jinja to?
    • Is it easy to comprehend and learn?
  • Ephemeral (CTE) was the unique selling point for you, why?
  • An engineer enthusiastic about a documentation feature… that asks for an explanation.

Within bol.com

  • Usage started as an experiment, how are we using it in bol.com?
  • Can you share some learnings you and your team had when you started using DBT?

Guests
Sander Boumeester – Software Engineer in the experimentation team.


Notes
On the website of getdbt there's a cool picture that explains the framework really well.

  continue reading

135 episodi

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