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Next-Gen Data Modeling, Integrity, and Governance with YODA

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Manage episode 357219000 series 2355972
Contenido proporcionado por Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka®. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

In this episode, Kris interviews Doron Porat, Director of Infrastructure at Yotpo, and Liran Yogev, Director of Engineering at ZipRecruiter (formerly at Yotpo), about their experiences and strategies in dealing with data modeling at scale.
Yotpo has a vast and active data lake, comprising thousands of datasets that are processed by different engines, primarily Apache Spark™. They wanted to provide users with self-service tools for generating and utilizing data with maximum flexibility, but encountered difficulties, including poor standardization, low data reusability, limited data lineage, and unreliable datasets.
The team realized that Yotpo's modeling layer, which defines the structure and relationships of the data, needed to be separated from the execution layer, which defines and processes operations on the data.
This separation would give programmers better visibility into data pipelines across all execution engines, storage methods, and formats, as well as more governance control for exploration and automation.
To address these issues, they developed YODA, an internal tool that combines excellent developer experience, DBT, Databricks, Airflow, Looker and more, with a strong CI/CD and orchestration layer.
Yotpo is a B2B, SaaS e-commerce marketing platform that provides businesses with the necessary tools for accurate customer analytics, remarketing, support messaging, and more.
ZipRecruiter is a job site that utilizes AI matching to help businesses find the right candidates for their open roles.
EPISODE LINKS

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Capíttulos

1. Intro (00:00:00)

2. What is Yotpo? (00:02:29)

3. Building an ETL framework based on Spark (00:05:25)

4. What is Apache Spark? (00:10:18)

5. Decoupling the data model (00:15:40)

6. Using data mesh principles (00:18:51)

7. How to address different data personas (00:22:24)

8. What is the "shift left" movement? (00:26:35)

9. How can organizations change the way they treat their data? (00:28:47)

10. Use-cases for tooling and documenting data sets (00:31:01)

11. Schema vs. schema-less (00:32:07)

12. What is YODA? (00:40:07)

13. Takeaways from the conversation with Doron and Liran (00:48:35)

14. It's a wrap! (00:52:45)

265 episodios

Artwork
iconCompartir
 
Manage episode 357219000 series 2355972
Contenido proporcionado por Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka®. Todo el contenido del podcast, incluidos episodios, gráficos y descripciones de podcast, lo carga y proporciona directamente Confluent, founded by the original creators of Apache Kafka® and Founded by the original creators of Apache Kafka® o su socio de plataforma de podcast. Si cree que alguien está utilizando su trabajo protegido por derechos de autor sin su permiso, puede seguir el proceso descrito aquí https://es.player.fm/legal.

In this episode, Kris interviews Doron Porat, Director of Infrastructure at Yotpo, and Liran Yogev, Director of Engineering at ZipRecruiter (formerly at Yotpo), about their experiences and strategies in dealing with data modeling at scale.
Yotpo has a vast and active data lake, comprising thousands of datasets that are processed by different engines, primarily Apache Spark™. They wanted to provide users with self-service tools for generating and utilizing data with maximum flexibility, but encountered difficulties, including poor standardization, low data reusability, limited data lineage, and unreliable datasets.
The team realized that Yotpo's modeling layer, which defines the structure and relationships of the data, needed to be separated from the execution layer, which defines and processes operations on the data.
This separation would give programmers better visibility into data pipelines across all execution engines, storage methods, and formats, as well as more governance control for exploration and automation.
To address these issues, they developed YODA, an internal tool that combines excellent developer experience, DBT, Databricks, Airflow, Looker and more, with a strong CI/CD and orchestration layer.
Yotpo is a B2B, SaaS e-commerce marketing platform that provides businesses with the necessary tools for accurate customer analytics, remarketing, support messaging, and more.
ZipRecruiter is a job site that utilizes AI matching to help businesses find the right candidates for their open roles.
EPISODE LINKS

  continue reading

Capíttulos

1. Intro (00:00:00)

2. What is Yotpo? (00:02:29)

3. Building an ETL framework based on Spark (00:05:25)

4. What is Apache Spark? (00:10:18)

5. Decoupling the data model (00:15:40)

6. Using data mesh principles (00:18:51)

7. How to address different data personas (00:22:24)

8. What is the "shift left" movement? (00:26:35)

9. How can organizations change the way they treat their data? (00:28:47)

10. Use-cases for tooling and documenting data sets (00:31:01)

11. Schema vs. schema-less (00:32:07)

12. What is YODA? (00:40:07)

13. Takeaways from the conversation with Doron and Liran (00:48:35)

14. It's a wrap! (00:52:45)

265 episodios

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