Free Tool

Lakehouse, Mesh, or Fabric?

Seven inputs covering timeline, budget, organisational autonomy, and how your data is actually used. A weighted algorithm returns a clear recommendation before you commit months to the wrong architecture.

Listen to this briefing

Pick your Lakehouse, Mesh, or Fabric

0:00

Your Situation

Timeline Urgency3

How quickly do you need this data foundation ready for your first AI use case?

12+ months acceptableNeed results under 6 months
Budget Available3

What's your implementation budget for this data foundation?

Lean, limited spendSubstantial, enterprise-scale
Organisational Autonomy3

How independently do your business units already manage their own data?

Centralised IT owns everythingBusiness units already independent
Analytical Workload Intensity3

How much of your AI roadmap depends on heavy cross-system analytics, batch reporting, or model training?

Mostly simple lookupsHeavy batch analytics & training
Live Query Latency Sensitivity3

How much does your AI use case depend on fast, real-time answers at query time?

Latency doesn't matterMust be near-instant
Number of Distinct Data Silos3

How many separate systems, departments, or business units hold data you need connected?

A few, centralisedMany, highly fragmented
Legacy Migration Tolerance3

How willing are you to migrate data out of existing legacy systems into a new store?

Systems must stay untouchedHappy to migrate everything

Your Recommendation

DATA MESH

Federated Ownership

Treat data like a product, with each business team owning and maintaining its own. Scales well when the people closest to the data are already positioned to keep it accurate.

LAKEHOUSE50%
DATA MESH50%
DATA FABRIC50%

Key factors driving this recommendation

  • Balanced inputs keep all three paths viable; adjust the sliders to reflect your actual situation

Scores are weighted across seven dimensions, each normalised so a neutral 3 sits at a true midpoint. Adjust inputs to stress-test the decision before committing months of build time.

Opens a 3-page report with your inputs, calculated below. Print or save it as a PDF from there.

Centralised Lakehouse

When it applies

You have budget and runway for a 12–18 month build, your AI roadmap leans heavily on cross-system analytics or model training, and you'd rather control everything from one place than negotiate ownership across business units.

Typical profile
  • Timeline: 12–18 months to production
  • Cost: Highest upfront investment of the three paths
  • Risk: Can become a new bottleneck if one team controls all access
  • Upside: Fastest for big, cross-system analytical questions once built

Data Mesh

When it applies

Business units already operate with real autonomy, you have many distinct data silos rather than a few big ones, and you're prepared to invest in the cultural shift of treating data as a product each team owns, not just the technical build.

Typical profile
  • Timeline: 9–15 months to production
  • Cost: Moderate infrastructure spend, real organisational investment
  • Risk: Harder to govern consistently across many independent teams
  • Upside: Scales well; the people closest to the data keep it accurate

Data Fabric

When it applies

You need something working fast, budget is limited, and disturbing legacy systems is not an option. A virtual metadata catalog connects existing systems in place without moving the underlying data.

Typical profile
  • Timeline: 3–6 months to a working data layer
  • Cost: Lowest upfront spend of the three paths
  • Risk: Can be slower than a Lakehouse when AI needs to search data live
  • Upside: Fastest to deploy; barely disturbs old systems