tgroenwals shared this post · May 11

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…

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Greg Coquillo The medallion architecture reference under lakehouse reflects how layered refinement models became standard for balancing raw ingestion with trusted analytics outputs. May 9 1 like

Sunjana Ramana Streaming and batch unified inside lakehouse architecture explains why many teams are consolidating separate analytics pipelines into broader platform strategies now. May 9 1 like

tgroenwals shared this post · Apr 22

Sumit Gupta

The best data pipeline is not the most complex one. It is the right one for your workload.

Teams often copy architectures they see online, then wonder why costs rise, latency increases, or maintenance becomes painful.

The smarter question is not “What is popular?”
It is “What pattern fits my data, scale, and business need?”

Here are 8 data pipeline patterns every data team should understand 👇

ETL (Extract, Transform, Load)
Transform first, then load clean data into storage.
ELT (Extract, Load, Transform)
Load raw data first, transform inside modern warehouses.

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Hari Prasad Renganathan Lambda architecture still teaches a useful principle: balancing speed and accuracy often requires separate paths with different responsibilities. Apr 21 1 like

Monu Yadav What stands out most is that pipeline choice depends on latency, scale, governance, and operational skill, not fashion. Apr 21 1 like

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