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Why is modern data architecture so confusing? And what made sense for me

Learn how data warehouse architecture works, compare models like star, vault, and lakehouse, and explore diagrams, real-world examples, and best practices.

We’ll walk you through the core components, types of architectures(from classic three-tier models to modern cloud-native designs), schema patterns, and emerging trends like lakehouse, data mesh, and real-time pipelines. Operational databases(e.g., PostgreSQL, Oracle, SQL Server) Enterprise applications like CRM or ERP systems External platforms via APIs (e.g., Salesforce, Google Ads) Cloud storage(e.g., Amazon S3, Azure Blob) Flat files and logs Streaming data from IoT devices or real-time feeds (e.g., Kafka) Track compute vs storage usage separately (especially in elastic architectures) Use auto-scaling and pause/resume features where possible Avoid long-running queries or unoptimized dashboards Watch for hidden costs in orchestration or data movement (e.g., cross-region transfers)

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