Build Real Time Applications With Operational Simplicity Using Dozer
Data Engineering Podcast
Dozer: An Open Source Engine for Data Ingestion, Transformation and API Generation for Real Time Sources
Dozer is an open source engine that includes data ingestion, transformation and API generation for real time sources. Matteo Palate has been in engineering for the last 20 years working on data. Dozer was inspired by Netflix's Bull Dozer project which solves a similar problem.
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Summary
Real-time data processing has steadily been gaining adoption due to advances in the accessibility of the technologies involved. Despite that, it is still a complex set of capabilities. To bring streaming data in reach of application engineers Matteo Pelati helped to create Dozer. In this episode he explains how investing in high performance and operationally simplified streaming with a familiar API can yield significant benefits for software and data teams together.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I'm interviewing Matteo Pelati about Dozer, an open source engine that includes data ingestion, transformation, and API generation for real-time sources
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Dozer is and the story behind it?
- What was your decision process for building Dozer as open source?
- As you note in the documentation, Dozer has overlap with a number of technologies that are aimed at different use cases. What was missing from each of them and the center of their Venn diagram that prompted you to build Dozer?
- In addition to working in an interesting technological cross-section, you are also targeting a disparate group of personas. Who are you building Dozer for and what were the motivations for that vision?
- What are the different use cases that you are focused on supporting?
- What are the features of Dozer that enable engineers to address those uses, and what makes it preferable to existing alternative approaches?
- Can you describe how Dozer is implemented?
- How have the design and goals of the platform changed since you first started working on it?
- What are the architectural "-ilities" that you are trying to optimize for?
- What is involved in getting Dozer deployed and integrated into an existing application/data infrastructure?
- How can teams who are using Dozer extend/integrate with Dozer?
- What does the development/deployment workflow look like for teams who are building on top of Dozer?
- What is your governance model for Dozer and balancing the open source project against your business goals?
- What are the most interesting, innovative, or unexpected ways that you have seen Dozer used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Dozer?
- When is Dozer the wrong choice?
- What do you have planned for the future of Dozer?
Contact Info
- @pelatimtt on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links
- Dozer
- Data Robot
- Netflix Bulldozer
- CubeJS
- JVM == Java Virtual Machine
- Flink
- Airbyte
- Fivetran
- Delta Lake
- LMDB
- Vector Database
- LLM == Large Language Model
- Rockset
- Tinybird
- Rust Language
- Materialize
- RisingWave
- DuckDB
- DataFusion
- Polars
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
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- Rudderstack:  Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at [dataengineeringpodcast.com/rudderstack](https://www.dataengineeringpodcast.com/rudderstack)
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