Multi-Cloud and Hybrid Data Strategies

Multi-cloud data engineering means using services from more than one cloud provider in the same data platform — AWS S3 as the data lake, Snowflake (running on AWS or Azure) as the warehouse, and Azure Active Directory for identity management. Hybrid data engineering extends this to include on-premises systems alongside cloud services — an Oracle database on-premises feeding an Azure Synapse warehouse in the cloud. Neither strategy is inherently better than single-cloud; they reflect organisational constraints (existing contracts, data residency requirements, vendor risk diversification) rather than purely technical choices.

The data gravity problem is the primary technical challenge in multi-cloud architectures: data accumulates where it was first created, and moving it across clouds incurs significant egress charges and latency. AWS charges $0.09/GB for data transfer out; GCP charges $0.08/GB. Moving 10TB from AWS S3 to GCP BigQuery costs approximately $900 in egress fees alone. Analytical workloads should run in the same cloud as the data they process, even if the organisation has a strategic preference for a different cloud for other workloads.

Analogy🏏Cricket

🏏 Think of it like cricket: This exercise is the IPL official statistics team building their daily automated processing pipeline — the complete workflow that takes raw ball-by-ball records from every ground and produces the certified statistics published on the official website by midnight. Stage 1 is the data catalogue check: verify that the incoming scorecards match the expected format before any processing begins. Stage 2 is the statistics calculation: joins with match metadata, derivation of over-level stats, economy rate computation. Stage 3 is the official record update: load the new statistics into the production database using the certified upsert protocol — delete the old version of today's record and insert the freshly computed one — so no match ever has two records in the official database.