Comparison of three orchestration tools and how they handle ingestion, ETL, and transformation across cloud platforms using code, low-code, or native Spark workflows.
How AI SQL functions in Snowflake plus Apache Iceberg make large-scale, SQL-native text processing and lakehouse operations seamless and scalable.
Most AI startups are fragile wrappers over rented intelligence. This post warns of their looming collapse and highlights what it takes to survive the next wave.
Real-time fraud detection with CEP and streaming data — from impossible travel to suspicious transaction spikes, it’s all caught in-flight.
MCP is a new protocol standardizing how AI agents interact with APIs. Like HTTP for agents, it solves tool-use chaos and promotes a shared interface.
DuckLake simplifies data lakes by using SQL databases for metadata and Parquet for storage. It’s fast, transactional, and DuckDB-native.
Roman uses MCP and Claude Desktop to build a schema registry tool that replaces complex UIs with conversational commands.
Spark 4.0 brings SQL scripting, native plotting in PySpark, multi-language support in Spark Connect, and major streaming and API improvements.
A Rust-based rewrite of dbt Core with better speed, strict YAML validation, SQL dialect support, and modern dev tooling. Built for the future of data transformation.
Jay Parikh, Scott Guthrie & others break down Day 1 announcements—Copilot, Azure, GitHub, and what developers need to navigate the AI era.
Build a system that reads and queries legal contracts using a graph-based RAG setup. Great example of structured retrieval for messy data.
Going distroless, multi-stage builds, and running as non-root helped trim a Node.js Docker image from 380MB to 60MB. Cleaner, faster, and more secure.
The Model Communication Protocol could be the standard for how AI agents talk to tools and APIs. Still early, but full of potential.