Tag: Data Engineering
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Introduction to ETL and ELT patterns
ETL and ELT are core data integration patterns that define how organizations move, transform, and analyze information. This post introduces both approaches, their architectures, trade-offs, and modern tooling, helping data engineers understand when to apply each and how to align them with modern cloud-native practices.
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Introduction to modern data warehouse design
Modern data warehouse design combines scalability, flexibility, and cost efficiency. This post introduces the fundamentals of data warehousing architecture, from schema models to ELT workflows, cloud-native platforms, and governance frameworks. Itâs a complete primer for engineers starting in data warehousing.
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Empirical: Airflow vs Prefect performance comparison
This empirical benchmark compares Apache Airflow and Prefect in real-world orchestration scenarios. Through detailed performance testing, it reveals how each handles scalability, latency, and fault recovery under heavy workloads in 2025 environments.
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Tools: AWS Lake Formation, Glue Data Catalog
AWS Lake Formation and Glue Data Catalog are two powerful services that streamline data lake management, access control, and metadata organization. This post explores how these tools integrate to build secure, discoverable, and governed data ecosystems on AWSâcovering architecture, best practices, and real-world enterprise use cases.