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.
610 articles from this blog
Guide on integrating Apache Druid with Dremio Cloud to enable SQL joins, AI analytics, and governance for real-time data.
Guide on connecting AWS Glue Data Catalog to Dremio Cloud for querying and managing AWS Iceberg tables with full DML support and federation.
Guide on connecting Snowflake Open Catalog to Dremio Cloud for multi-engine Apache Iceberg analytics, federation, and cost optimization.
Guide on connecting Databricks Unity Catalog to Dremio Cloud to query Delta Lake tables with federation, AI analytics, and performance acceleration.
Guide on connecting any Apache Iceberg REST Catalog to Dremio Cloud for universal lakehouse data access and management.
Introduces Dremio's built-in Open Catalog for Apache Iceberg, offering a zero-configuration, production-ready lakehouse solution with automated management.
Guide on connecting self-managed Dremio Software to Dremio Cloud for hybrid data federation, enabling queries across on-prem and cloud data sources.
Explains the difference between a metrics layer and a semantic layer in data architecture, clarifying their distinct roles and relationship.
Explains the distinct roles of data catalogs and semantic layers in data architecture, arguing they are complementary tools.
Explains why AI data analytics fail without a semantic layer to define business metrics and ensure accurate, secure queries.
Explains how a semantic layer enforces data governance by embedding policies directly into the query path, ensuring consistent metrics and access control.
Explains how data virtualization and a semantic layer enable querying distributed data without copying, reducing costs and improving freshness.
Explains Headless BI and how a universal semantic layer centralizes metric definitions to replace tool-specific models, enabling consistent analytics.
Explains how a self-documenting semantic layer uses AI to automate data documentation, reducing manual work and governance risks for data teams.
Seven critical mistakes that can derail semantic layer projects in data engineering, with practical advice on how to avoid them.
A guide to the core principles and systems thinking required for data engineering, beyond just learning specific tools.
A guide to designing reliable, fault-tolerant data pipelines with architectural principles like idempotency, observability, and DAG-based workflows.
Argues that data quality must be enforced at the pipeline's ingestion point, not patched in dashboards, to ensure consistent, reliable data.
Explains idempotent data pipelines, patterns like partition overwrite and MERGE, and how to prevent duplicate data during retries.
Explains how to safely evolve data schemas using API-like discipline to prevent breaking downstream systems like dashboards and ML pipelines.
