#Data Warehouse

Data warehouse architecture organises analytical data for efficient querying and reporting. Posts cover BigQuery as a data warehouse platform, FinOps cost attribution, and the storage and query patterns that make large-scale analytical workloads both performant and cost-effective.

2 posts tagged with data warehouse. ← All posts

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Pratik Dhanave · ·7 min read

Where Data Lives: Warehouses, Lakes, and Lakehouses

"Just put it in a database" stops working the moment you're dealing with analytics at scale — because the database that runs your application is optimized for exactly the wrong thing. Analytical data needs different storage: systems built to scan and aggregate huge volumes, not to serve fast individual transactions. The landscape of analytical storage — data warehouses, data lakes, and the newer lakehouses — is where data engineering decisions about where data lives get made, and understanding the differences (starting with OLTP vs OLAP) is essential.

'Just put it in a database' stops working the moment you're doing analytics at scale — the database that runs your app is optimized for exactly the wrong thing. Analytical data needs different storage: warehouses, lakes, and lakehouses. Understanding the differences (starting with OLTP vs OLAP) is essential.

#Data Engineering#Data Warehouse#Data Lake#Backend Engineering#Data#Databases

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.