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.
A guide to choosing between batch and streaming data processing models based on actual freshness requirements and cost.
Explains the importance of automated testing for data pipelines, covering schema validation, data quality checks, and regression testing.
Explains the importance of pipeline observability for data health, covering metrics, logs, and lineage to detect issues beyond simple execution monitoring.
A practical, tool-agnostic checklist of essential best practices for designing, building, and maintaining reliable data engineering pipelines.
Seven common data modeling mistakes that cause reporting errors and slow analytics, with practical solutions to avoid them.
Seven critical mistakes that can derail semantic layer projects in data engineering, with practical advice on how to avoid them.
Explores the limitations of traditional pull queries in data systems and advocates for using materialized views and data duplication to improve performance.
A comprehensive guide to learning Apache Iceberg, data lakehouse architecture, and Agentic AI with curated tutorials, tools, and resources.
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 monthly roundup of 78 curated links on data engineering, architecture, AI, and tech trends, with top picks highlighted.
A monthly roundup of curated links and articles focused on data engineering, Apache Kafka, and data platform technologies.
A guide to scheduling compaction and snapshot expiration in Apache Iceberg tables based on workload patterns and infrastructure constraints.
A monthly roundup of data engineering links covering Apache Iceberg, Kafka, Debezium, Spark, and lakehouse architecture.
Explains how Apache Iceberg tables degrade without optimization, covering small files, fragmented manifests, and performance impacts.
Explains the importance of table maintenance in Apache Iceberg for data lakehouses, covering metadata and file management.
An analysis of DuckLake, a new open table format and catalog specification for data engineering, comparing it to existing solutions like Iceberg and Delta Lake.
A monthly roundup of curated links and articles covering data engineering, Kafka, stream processing, and AI, with top picks highlighted.
