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.
418 articles from this blog
A guide to designing reliable, fault-tolerant data pipelines with architectural principles like idempotency, observability, and DAG-based workflows.
Explains why AI data analytics fail without a semantic layer to define business metrics and ensure accurate, secure queries.
Explains database denormalization: when to flatten data for faster analytics queries and when to avoid it.
A practical, tool-agnostic checklist of essential best practices for designing, building, and maintaining reliable data engineering pipelines.
Explains the three levels of data modeling (conceptual, logical, physical) and their importance in database design.
A comprehensive guide exploring the taxonomy, tools, and best practices for using AI-assisted coding tools in modern software development.
Explains Recursive Language Models (RLMs), which are LLMs that call themselves to break complex tasks into structured, reusable steps.
A 2025 year-in-review of key Apache data projects: Iceberg, Polaris, Parquet, and Arrow, detailing their major updates and future roadmap.
Introduces DremioFrame and IceFrame, two new Python libraries for simplifying work with Dremio and Apache Iceberg tables.
Introduces dremioframe, a Python DataFrame library for querying Dremio with a pandas-like API, generating SQL under the hood.
A hands-on tutorial exploring Dremio Cloud Next Gen's new free trial, covering its lakehouse platform, AI features, and SQL capabilities.
A comprehensive guide to learning Apache Iceberg, data lakehouse architecture, and Agentic AI with curated tutorials, tools, and resources.
Explores the commercial Apache Iceberg catalog ecosystem, focusing on REST Catalog standards, optimization strategies, and architectural trade-offs.
Explores two paths for building a universal lakehouse catalog that extends beyond Apache Iceberg tables to manage diverse data formats and sources.
A technical guide on using Apache Iceberg with Apache Spark and Polaris for building and managing a data lakehouse, covering setup, operations, and optimization.
Overview of key proposals in Apache Iceberg v4, focusing on performance, metadata efficiency, and portability for modern data workloads.
A comprehensive guide comparing five major open table formats (Iceberg, Delta Lake, Hudi, Paimon, DuckLake) for modern data lakehouses, covering their internals and use cases.
A comprehensive guide to the data lakehouse architecture, its core components (Iceberg, Delta, Hudi, Paimon), and the surrounding ecosystem for modern data platforms.
A guide to building an autonomous, self-healing optimization pipeline for Apache Iceberg tables to maintain performance and cost efficiency.
Strategies for scaling and optimizing Apache Iceberg data compaction jobs, including parallelism, checkpointing, and failure recovery.
