Apache Arrow integration in mssql-python enables zero-copy, columnar data fetching from SQL Server, boosting speed and memory efficiency for Polars, Pandas, DuckDB, and more.
mssql-python now supports Apache Arrow for zero-copy, columnar data fetching from SQL Server, boosting performance for Polars, Pandas, and DuckDB workflows.
Apache Arrow support in mssql-python brings zero-copy, columnar fetches for Polars, Pandas, DuckDB, with speed and memory gains.
Explore eight surprising Python challenges—from packaging apps to SQLite backups—and learn practical solutions for each.
Microsoft's ConferencePulse, built on .NET's composable AI stack, demonstrates unified abstractions for live polls, Q&A, and session summarization, aiming to reduce fragmentation in AI development.
Manually creating SQL Server database in SSMS before Laravel migrations resolves common 'Login failed' error, developer finds.
Breaking: Data processing dilemma solved? Experts shift focus from batch vs stream to timing of data analysis, urging organizations to consider latency needs.
Breaking: mssql-python now supports Apache Arrow for direct columnar data transfer, eliminating Python object overhead and boosting performance for SQL Server + Polars/Pandas workflows.
Learn how a 3 GB SQLite database was replaced with a 10 MB finite state transducer binary, achieving 99.7% size reduction and faster lookups for static key-value datasets.
Microsoft demonstrates ConferencePulse, a live conference assistant built with a new composable AI stack for .NET, promising unified abstractions for AI models, vector databases, and agent orchestration.
Python developers face significant challenges in creating standalone apps, backing up SQLite databases, and installing on air-gapped systems. New language features aim to address some issues, but tooling gaps remain.
mssql-python now supports Apache Arrow, enabling zero-copy data transfer to Polars, Pandas, and DuckDB, boosting speed and memory efficiency.
A step-by-step tutorial for non-coding engineers to leverage AI via the 5-layer diagonal-axis framework, turning trivial tech into cross-industry tools.
Explore how ConferencePulse uses .NET's composable AI stack—including Microsoft.Extensions.AI, VectorData, and the Agent Framework—to build a live conference app with polls, RAG Q&A, and summaries.
Discover the 7 essential .NET composable AI components used to build ConferencePulse, an interactive conference app with live polls, RAG Q&A, and multi-agent summaries.
Explore how scenario modelling, calibrated uncertainty, and historical error provide more honest insights for English local elections than traditional forecasting.
A real-world data workflow rewrite from Pandas to Polars achieved a 300x speedup (61s to 0.20s), driven by lazy execution, multi-threading, and expression-based transformations. Key mental model shift from eager to lazy evaluation.
Explore scenario modelling for English local elections, where calibrated uncertainty and historical error analysis reveal that models are most useful when they refuse to make a single forecast.
A real Pandas workflow running in 61 seconds was rewritten in Polars, achieving 0.2 seconds. This article explores Polars’ performance advantages, the lazy evaluation mental model, and practical tips for adoption.
Discover how .NET's modular AI stack powers ConferencePulse—a Blazor Server app for live polls, Q&A, and session summaries using composable building blocks.