The convergence of data engineering, data science, and machine learning has created unprecedented demand for unified analytics platforms that can handle diverse workloads without the complexity of managing multiple disconnected systems. Azure Databricks represents a compelling answer to this challenge—a collaborative Apache Spark-based analytics platform optimized for the Microsoft Azure cloud. Having architected data platforms… Continue reading
Tag: Data Engineering
Azure Synapse Analytics: A Solutions Architect’s Guide to Unified Data Analytics
The modern enterprise data landscape demands more than traditional data warehousing or isolated analytics solutions. Organizations need unified platforms that can handle everything from batch ETL processing to real-time streaming analytics, from structured data warehousing to exploratory data science workloads. Azure Synapse Analytics represents Microsoft’s answer to this challenge—a comprehensive analytics service that brings together… Continue reading
Azure Data Factory: A Solutions Architect’s Guide to Enterprise Data Integration
Enterprise data integration has evolved from simple ETL batch jobs to sophisticated orchestration platforms that handle diverse data sources, complex transformations, and real-time processing requirements. Azure Data Factory represents Microsoft’s cloud-native answer to these challenges, providing a fully managed data integration service that scales from simple copy operations to enterprise-grade data pipelines. Having designed and… Continue reading
Data Lakehouse Architecture: Bridging Data Lakes and Data Warehouses
After two decades of building data platforms, I’ve witnessed the pendulum swing between data lakes and data warehouses multiple times. Organizations would invest heavily in one approach, hit its limitations, then pivot to the other. The data lakehouse architecture represents something different—a genuine synthesis that addresses the fundamental trade-offs that forced us to choose between… Continue reading
Tips and Tricks – Implement Idempotent ETL with Merge Statements
Use MERGE (upsert) for safe, rerunnable data pipelines that handle duplicates gracefully.
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