Moving large JSON payloads from PostgreSQL TOAST tables to Parquet on S3 with deterministic sharding, row-group pruning, and range-based reads for millisecond point lookups.| Shayon Mukherjee
Financial data is growing at a pace that is hard to keep up with. Every tick, quote, and trade across multiple exchanges adds up fast. Open formats provide the answer. Instead of locking your data into one vendor's ecosystem, open standards let it move freely. This resonates well with QuestDB, whose mission has always been to provide the fastest time series database. It embraces open formats, so Parquet support comes naturally.| QuestDB Blog
A database engineer at QuestDB discovers that an AI-suggested optimization for array shape calculation in their Parquet reader actually made the code slower, and achieves a 5x average speedup by applying a few simple optimizations.| QuestDB Blog
Getting different parties in the software industry to agree on a common standard is rare. Most of the time, a dominant player sets the rules. Occasionally, however, collaboration happens organicall…| Small Data And self service
A common task in S3-based Data Lakes is to repartition data, to optimize query patterns and speed. This article describes a serverless solution using DuckDB| tobilg.com
Discover Project Antalya: Experience ClickHouse® analytics on Iceberg storage, cutting costs by 90% and delivering up to 100x faster queries.| Altinity | Run open source ClickHouse® better
Dremio version 25 redefines SQL analytics with enhanced memory management, spillable joins, and proactive memory control.| Dremio
