Alex Merced — Developer and technical writer sharing in-depth insights on data engineering, Apache Iceberg, data lakehouse architectures, Python tooling, and modern analytics platforms, with a strong focus on practical, hands-on learning.
574 articles from this blog
A guide to evaluating agentic analytics tools for enterprise AI, focusing on governance, semantics, and production readiness.
Explains the fundamental divide between operational (OLTP) and analytical (OLAP) systems, the physics enforcing it, and the truth about hybrid architectures.
Explores combining Apache Iceberg REST Catalogs and Arrow Flight for high-performance columnar data transfers in lakehouse architectures.
Explores fine-grained security for AI agents in analytics, covering dynamic access control, identity propagation, and governance at machine speed.
Explores an architecture pattern combining Amazon S3 Tables and MCP for governed conversational AI access to Iceberg data.
Explores moving from passive BI dashboards to closed-loop decision agents that observe, reason, validate, and act on data within a lakehouse architecture.
A comprehensive guide to streaming data into Apache Iceberg tables in 2026, covering latency, tools, and architectures for sub-second freshness.
Explains designing idempotent pipelines in agentic lakehouses to prevent double-write anomalies using Iceberg and workflow safeguards.
Explores context engineering for AI agents, comparing personal vs. shared context to prevent failures in human and organizational AI use.
Technical guide on preparing data lakehouses for EU AI Act compliance, focusing on auditable lineage and data provenance.
Explains file encryption for lakehouses, covering Parquet Modular Encryption, Iceberg table encryption, and interoperability challenges.
Designing secure, air-gapped data lakehouses using Apache Iceberg for defense, healthcare, finance, and other high-security sectors.
Explains how to prepare a data lakehouse for EU AI Act compliance, focusing on data lineage and provenance.
Explores using PyIceberg without Apache Spark for Python-based Iceberg table operations, focusing on architecture, boundaries, and production patterns.
Explores how schema contracts, semantic models, and governed lakehouse design improve Microsoft Fabric agentic analytics and AI behavior.
Analysis of how enterprise AI value comes from governed context and data contracts, not just model choice.
Analysis of Apache Iceberg v4 performance focusing on metadata round trips, root manifests, and object storage latency for platform engineers.
Explores event-driven table compaction using agents in lakehouse architectures, focusing on small file problems and production patterns.
Analysis of LTAP (Lakehouse Transactional Analytical Processing) focusing on freshness, isolation, and workload boundaries for data architects.
Article on fine-grained security for AI agents, focusing on identity, masking, and policy enforcement in machine-speed analytics.
