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Related Articles from SNS
Stabilizing the parquet problem
arXiv:2606.04936v1 Announce Type: cross Abstract: We systematically analyze the stability of the iterative solution of the parquet equations by studying the spectrum of the Jacobian associated with the commonly used damped fixed-point iteration procedure. In this context, we provide an explicit criterion that determines when the physical fixed point of the parquet iteration becomes unstable. Importantly, we demonstrate that misleading convergence issues, observed in parquet calculation at...
Show HN: Streambed – Stream Postgres to Iceberg on S3, Supports Postgres Wire
Postgres-to-Iceberg CDC engine. Offload analytical queries from your production database without changing your application. streambed streams WAL changes via logical replication, writes Parquet files to S3, and commits Iceberg metadata.
Kore: Binary File Format Optimized for Modern Data Systems (Open Source)
The fastest, most compressed columnar format for big data | v0.1.0 KORE is a high-performance binary file format optimized for analytical workloads. It provides: - 38% compression ratio (vs 63% for Parquet) - 131x query speedup with column pruning & predicate pushdown - Zero data loss verification (400K+ cells tested) - Native Spark integration — read/write with PySpark Add this crate as a dependency (when published) or include from path: use kore_fileformat::*; // Write data...
AWS whips out Graviton-powered Redshift instances, claims 7x speed for data warehouse
AWS has introduced Graviton-powered Redshift RG instances, claiming they can accelerate new query workloads by up to seven times. These instances offer significant performance improvements and cost efficiencies compared to previous generations, enabling Redshift to better handle increasing demands from AI agent workloads. The updated engine also allows users to run SQL analytics across both data warehouses and data lakes from a single platform.
MLSkip: Data Skipping for ML Filters via Lightweight Metadata
arXiv:2606.03946v1 Announce Type: new Abstract: Database vendors recently released AI functions that can be used in filter predicates. As such functions often rely on costly, black-box ML models, they unveil new data management challenges. Concretely, traditional data skipping techniques for integer and string data fail to be applicable to the new filter type.