𝐈𝐟 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 "𝐰𝐡𝐢𝐜𝐡 𝐭𝐨𝐨𝐥," 𝐢𝐭'𝐬 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐰𝐫𝐨𝐧𝐠.

It should start with which problem.

Mesh, Fabric, and Lakehouse get pitched like rival platforms. They're not. They answer three completely different questions.

Use this to decide 👇

𝐈𝐬 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐩𝐞𝐨𝐩𝐥𝐞? → 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡
→ Central data team is the bottleneck
→ Domains can't move fast enough
→ Need: domain ownership, data-as-product, federated governance, SLA contracts

𝐈𝐬 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧? → 𝐃𝐚𝐭𝐚 𝐅𝐚𝐛𝐫𝐢𝐜
→ Data scattered across cloud, SaaS, on-prem
→ Manual lineage, policy gaps, slow discovery
→ Need: active metadata, knowledge graph, virtualization, AI/ML augmentation, real-time discovery

𝐈𝐬 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐬𝐭𝐨𝐫𝐚𝐠𝐞? → 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞𝐡𝐨𝐮𝐬𝐞
→ Lake too messy, warehouse too rigid
→ Duplicating data for BI vs ML
→ Need: ACID transactions, Delta/Iceberg/Hudi, BI + ML on one copy, streaming + batch unified

𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭: Stop asking "which architecture?" Start asking "which bottleneck?"

The right answer often involves all three, but in the right order.

What's your biggest data bottleneck right now?

Follow Sumit Gupta for more such insights!!