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.
501 articles from this blog
Explains the importance of automated testing for data pipelines, covering schema validation, data quality checks, and regression testing.
Explains data partitioning and organization strategies to drastically improve query performance in analytical databases.
Explains the importance of pipeline observability for data health, covering metrics, logs, and lineage to detect issues beyond simple execution monitoring.
Explains idempotent data pipelines, patterns like partition overwrite and MERGE, and how to prevent duplicate data during retries.
Explains how a semantic layer enforces data governance by embedding policies directly into the query path, ensuring consistent metrics and access control.
Explains the distinct roles of data catalogs and semantic layers in data architecture, arguing they are complementary tools.
Compares Star Schema and Snowflake Schema data models, explaining their structures, trade-offs, and when to use each for optimal data warehousing.
Explains why AI data analytics fail without a semantic layer to define business metrics and ensure accurate, secure queries.
A comprehensive guide to data modeling, explaining its meaning, three abstraction levels, techniques, and importance for modern data systems.
Explains how to safely evolve data schemas using API-like discipline to prevent breaking downstream systems like dashboards and ML pipelines.
Explains the difference between a metrics layer and a semantic layer in data architecture, clarifying their distinct roles and relationship.
Explains Headless BI and how a universal semantic layer centralizes metric definitions to replace tool-specific models, enabling consistent analytics.
A guide to choosing between batch and streaming data processing models based on actual freshness requirements and cost.
A guide to designing reliable, fault-tolerant data pipelines with architectural principles like idempotency, observability, and DAG-based workflows.
Explores how data modeling principles adapt for modern lakehouse architectures using open formats like Apache Iceberg and the Medallion pattern.
Explains dimensional modeling for analytics, covering facts, dimensions, grains, and table design for query performance.
Explains the three levels of data modeling (conceptual, logical, physical) and their importance in database design.
Argues that data quality must be enforced at the pipeline's ingestion point, not patched in dashboards, to ensure consistent, reliable data.
A guide to the core principles and systems thinking required for data engineering, beyond just learning specific tools.
A practical, tool-agnostic checklist of essential best practices for designing, building, and maintaining reliable data engineering pipelines.
