Database Daily·11wA real production SQL query showed me why working SQL isn’t always correct.
A blog post shares practical SQL lessons learned from a real production query, covering data grain mistakes like duplicate handling, CASE statement priority ordering, collation issues that affect joins, and techniques for writing queries that produce decision-ready results.
Data Engineering Blog·13wA Diary of a Data Engineer
Data engineering has evolved through multiple epochs from the 1970s to today, but the core loop remains unchanged: ingest, model, transform, serve, break, rebuild. Despite shifting from SSIS and star schemas to dbt and Iceberg, data engineers still solve the same fundamental problems with different tools. The role requires understanding business logic, data modeling fundamentals, and DevOps principles while accepting the paradox of being invisible when things work but scrutinized when they break. Success comes from mastering timeless fundamentals like SQL and dimensional modeling rather than chasing every new framework, talking to business stakeholders to understand why data matters, and building reliable foundations that enable better decision-making.
ClickHouse·12wFast, scalable, enterprise-grade Postgres natively integrated with ClickHouse
ClickHouse announces a managed Postgres service backed by NVMe storage, delivering up to 10X faster performance for disk-bound workloads. The service integrates natively with ClickHouse through CDC capabilities and the pg_clickhouse extension, enabling a unified data stack that combines transactional (Postgres) and analytical (ClickHouse) workloads. Built in partnership with Ubicloud, it offers enterprise-grade features including high availability, automatic backups, and point-in-time recovery, with all integration components being open source.
Lobsters·14wPostgreSQL 18 RETURNING Enhancements: A Game Changer for Modern Applications
PostgreSQL 18 introduces enhanced RETURNING clause capabilities with OLD and NEW aliases, allowing developers to capture both before and after states of data in INSERT, UPDATE, DELETE, and MERGE operations within a single atomic statement. This eliminates the need for separate SELECT queries, complex triggers, or application-level change tracking. The feature is particularly powerful for MERGE operations, enabling sophisticated upsert patterns with complete visibility into what changed. Practical examples demonstrate building audit trails, tracking inventory changes, and comparing values without additional database round trips or trigger overhead.
LangChain·12wHow Remote uses LangChain and LangGraph to onboard thousands of customers with AI
Remote built a Code Execution Agent using LangChain and LangGraph to automate customer data migrations during onboarding. The system separates LLM reasoning from code execution: models decide what transformations to perform, while Python code in a WebAssembly sandbox handles actual data processing. This hybrid approach bypasses context window limitations and hallucination risks by keeping large datasets outside the LLM's context. The agent transforms diverse HR and payroll data formats (CSV, Excel, SQL exports) into standardized JSON schemas, reducing migration time from days to hours while maintaining accuracy and auditability for compliance-critical operations.