Kostas Pardalis
@cpard.bsky.social
Building https://typedef.ai | host @ https://techontherocks.show | Done some cool stuff with trinodb | ex-RudderStack | previously CEO @ Blendo
That’s a different concept though, right? As you said, here you have a proxy and you pick a different query engine over the same storage. I think using the term federation in this case will confuse people. I can see how this pattern can work.
October 18, 2025 at 6:46 PM
That’s a different concept though, right? As you said, here you have a proxy and you pick a different query engine over the same storage. I think using the term federation in this case will confuse people. I can see how this pattern can work.
Full scans on different data sources that then need to be joined and a much closer to ETL workload. This will kill every federated query engine.
Plus what do you do when you have different semantic between different query engines? Let’s say how you handle decimal overflows.
Plus what do you do when you have different semantic between different query engines? Let’s say how you handle decimal overflows.
October 18, 2025 at 12:34 AM
Full scans on different data sources that then need to be joined and a much closer to ETL workload. This will kill every federated query engine.
Plus what do you do when you have different semantic between different query engines? Let’s say how you handle decimal overflows.
Plus what do you do when you have different semantic between different query engines? Let’s say how you handle decimal overflows.
Oh no. Trino tried tried to do that. You really can’t do it. The problem with federated queries is that they work well when you can push computation down to the query engine you federate at and get out a highly reduced dataset. That’s not the case with ETL though.
October 18, 2025 at 12:34 AM
Oh no. Trino tried tried to do that. You really can’t do it. The problem with federated queries is that they work well when you can push computation down to the query engine you federate at and get out a highly reduced dataset. That’s not the case with ETL though.
6/7
Performance & DX
Rust optimizations plus leaner default configs deliver performance gains and a frictionless setup experience.
so you spend less time tuning and more time building.
Performance & DX
Rust optimizations plus leaner default configs deliver performance gains and a frictionless setup experience.
so you spend less time tuning and more time building.
August 7, 2025 at 6:10 PM
6/7
Performance & DX
Rust optimizations plus leaner default configs deliver performance gains and a frictionless setup experience.
so you spend less time tuning and more time building.
Performance & DX
Rust optimizations plus leaner default configs deliver performance gains and a frictionless setup experience.
so you spend less time tuning and more time building.
5/7
New Functions & Models
Access built-in summarization, new semantic APIs, and multiple embedding providers (e.g. Cohere, Google Gemini) out of the box.
This broadens your toolkit, so you can prototype and productionize a wider range of AI workflows quickly.
New Functions & Models
Access built-in summarization, new semantic APIs, and multiple embedding providers (e.g. Cohere, Google Gemini) out of the box.
This broadens your toolkit, so you can prototype and productionize a wider range of AI workflows quickly.
August 7, 2025 at 6:10 PM
5/7
New Functions & Models
Access built-in summarization, new semantic APIs, and multiple embedding providers (e.g. Cohere, Google Gemini) out of the box.
This broadens your toolkit, so you can prototype and productionize a wider range of AI workflows quickly.
New Functions & Models
Access built-in summarization, new semantic APIs, and multiple embedding providers (e.g. Cohere, Google Gemini) out of the box.
This broadens your toolkit, so you can prototype and productionize a wider range of AI workflows quickly.
4/7
Composable Pipelines
Save intermediate DataFrames as persistent views in the fenic catalog.
Reuse and chain complex transformations across jobs without rewriting or rerunning upstream logic, accelerating iteration and collaboration.
Composable Pipelines
Save intermediate DataFrames as persistent views in the fenic catalog.
Reuse and chain complex transformations across jobs without rewriting or rerunning upstream logic, accelerating iteration and collaboration.
August 7, 2025 at 6:10 PM
4/7
Composable Pipelines
Save intermediate DataFrames as persistent views in the fenic catalog.
Reuse and chain complex transformations across jobs without rewriting or rerunning upstream logic, accelerating iteration and collaboration.
Composable Pipelines
Save intermediate DataFrames as persistent views in the fenic catalog.
Reuse and chain complex transformations across jobs without rewriting or rerunning upstream logic, accelerating iteration and collaboration.
3/7
Typed Semantics
Define your output schema once with Pydantic and get back validated, strongly typed results.
This enforces consistency, surfaces errors early, and eliminates manual parsing of LLM responses.
Typed Semantics
Define your output schema once with Pydantic and get back validated, strongly typed results.
This enforces consistency, surfaces errors early, and eliminates manual parsing of LLM responses.
August 7, 2025 at 6:10 PM
3/7
Typed Semantics
Define your output schema once with Pydantic and get back validated, strongly typed results.
This enforces consistency, surfaces errors early, and eliminates manual parsing of LLM responses.
Typed Semantics
Define your output schema once with Pydantic and get back validated, strongly typed results.
This enforces consistency, surfaces errors early, and eliminates manual parsing of LLM responses.
2/7
Robust Fuzzy Text Matching
Ground LLM outputs against your existing data: record linkage, deduplication, and typo-tolerant joins become first-class operations.
This improves precision in extraction pipelines and slashes downstream error rates.
Robust Fuzzy Text Matching
Ground LLM outputs against your existing data: record linkage, deduplication, and typo-tolerant joins become first-class operations.
This improves precision in extraction pipelines and slashes downstream error rates.
August 7, 2025 at 6:10 PM
2/7
Robust Fuzzy Text Matching
Ground LLM outputs against your existing data: record linkage, deduplication, and typo-tolerant joins become first-class operations.
This improves precision in extraction pipelines and slashes downstream error rates.
Robust Fuzzy Text Matching
Ground LLM outputs against your existing data: record linkage, deduplication, and typo-tolerant joins become first-class operations.
This improves precision in extraction pipelines and slashes downstream error rates.
