One month ago, I shared how we deployed a streaming SQL engine that lets you many data sources using standard SQL syntax.
Original post is here:
Today, I want to present our research paper: "Streaming SQL Engine: Lightweight Cross-Data Source Integration for Resource-Constrained Environments."
Read it here:
[https://github.com/Ierofantis/streaming_sql_engine/blob/master/Streaming_SQL_Engine_Paper.pdf](https://github.com/Ierofantis/streaming_sql_engine/blob/master/Streaming_SQL_Engine_Paper.pdf…
One month ago, I shared how we deployed a streaming SQL engine that lets you many data sources using standard SQL syntax.
Original post is here:
Today, I want to present our research paper: "Streaming SQL Engine: Lightweight Cross-Data Source Integration for Resource-Constrained Environments."
Read it here:
https://github.com/Ierofantis/streaming_sql_engine/blob/master/Streaming_SQL_Engine_Paper.pdf
The problem we aimed to solve is simple: many organizations need ETL and big data capabilities without the overhead of heavy infrastructure.
Not every team can spin up large compute clusters or dedicate substantial resources to data pipelines.
Our solution is a streaming, iterator-based SQL engine optimized for memory efficiency and cross-source integration.
This work isn’t about claiming the "fastest" solution, it’s about providing a practical, optimized tool that solves real problems for teams with limited resources.
All comparisons are transparent, with fair benchmarks.
I believe the best solutions come from honest evaluations of trade-offs, not marketing hype.