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.
425 articles from this blog
Explains core data engineering concepts: metadata, data lineage, and governance, and their importance for scalable, compliant data systems.
Explores the importance of data quality and validation in data engineering, covering key dimensions and tools for reliable pipelines.
An introduction to data engineering concepts, focusing on data sources and ingestion strategies like batch vs. streaming.
Explains the data lakehouse architecture, a unified approach combining data lake scalability with warehouse management features like ACID transactions.
Explains core data engineering concepts, comparing ETL and ELT data pipeline strategies and their use cases.
Explains streaming data fundamentals, how streaming systems work, their use cases, and challenges compared to batch processing.
An introduction to data warehousing concepts, covering architecture, components, and performance optimization for analytical workloads.
Explores the modern data stack, cloud platforms, and principles for building flexible, cloud-native data engineering architectures.
Explores workflow orchestration in data engineering, covering DAGs, tools, and best practices for managing complex data pipelines.
Explains the importance of data storage formats and compression for performance and cost in large-scale data engineering systems.
Explains data lakes, their key characteristics, and how they differ from data warehouses in modern data architecture.
An introductory guide to data engineering, explaining its role, key concepts, and how it differs from data science in the modern data ecosystem.
Explores how DevOps principles like CI/CD, infrastructure as code, and monitoring are applied to data engineering for reliable, scalable data pipelines.
Explains how Sampling and Prompts in the Model Context Protocol (MCP) enable smarter, safer, and more controlled AI agent workflows.
Explains how Tools in the Model Context Protocol (MCP) enable LLMs to execute actions like running commands or calling APIs, moving beyond just reading data.
Explains how the Model Context Protocol (MCP) uses 'Resources' to securely serve structured data from systems like files and databases to LLMs.
Explains the architecture of the Model Context Protocol (MCP), detailing its client-server model, core components, and message flow for connecting AI models to tools and data.
Explains the Model Context Protocol (MCP), an open standard for connecting AI agents and LLMs to external data sources and tools, enabling interoperability.
Explores AI agent frameworks, their benefits, limitations, and introduces the Model Context Protocol (MCP) for more modular AI systems.
Explores AI agents, their core components, differences from LLMs, and real-world applications, positioning them as the future of autonomous AI systems.
