Episode description
Episode description
SummaryIn this episode of the Data Engineering Podcast, the creators of Feldera talk about their incremental compute engine designed for continuous computation of data, machine learning, and AI workloads. The discussion covers the concept of incremental computation, the origins of Feldera, and its unique ability to handle both streaming and batch data seamlessly. The guests explore Feldera's architecture, applications in real-time machine learning and AI, and challenges in educating users about incremental computation. They also discuss the balance between open-source and enterprise offerings, and the broader implications of incremental computation for the future of data management, predicting a shift towards unified systems that handle both batch and streaming data efficiently.
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 Leonid Ryzhyk, Lalith Suresh, and Mihai Budiu about Feldera, an incremental compute engine for continous computation of data, ML, and AI workloads
- Introduction
- Can you describe what Feldera is and the story behind it?
- DBSP (the theory behind Feldera) has won multiple awards from the database research community. Can you explain what it is and how it solves the incremental computation problem?
- Depending on which angle you look at it, Feldera has attributes of data warehouses, federated query engines, and stream processors. What are the unique use cases that Feldera is designed to address?
- In what situations would you replace another technology with Feldera?
- When is it an additive technology?
- Can you describe the architecture of Feldera?
- How have the design and scope evolved since you first started working on it?
- What are the state storage interfaces available in Feldera?
- What are the opportunities for integrating with or building on top of open table formats like Iceberg, Lance, Hudi, etc.?
- Can you describe a typical workflow for an engineer building with Feldera?
- You advertise Feldera's utility in ML and AI use cases in addition to data management. What are the features that make it conducive to those applications?
- What is your philosophy toward the community growth and engagement with the open source aspects of Feldera and how you're balancing that with sustainability of the project and business?
- What are the most interesting, innovative, or unexpected ways that you have seen Feldera used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Feldera?
- When is Feldera the wrong choice?
- What do you have planned for the future of Feldera?
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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- Feldera
- DBSP paper
- Differential Dataflow
- Trino
- Flink
- Spark
- Materialize
- Clickhouse
- DuckDB
- Snowflake
- Arrow
- Substrait
- DataFusion
- DSP == Digital Signal Processing
- CDC == Change Data Capture
- PRQL
- LSM (Log-Structured Merge) Tree
- Iceberg
- Delta Lake
- Open VSwitch
- Feature Engineering
- Calcite