Buying Data Platform

Buyer's guide.

Overview

An end-to-end data platform combines storage, transformation, orchestration, and serving in one bill. Vendors range from "Snowflake plus dbt plus Fivetran" stitched together, through Databricks lakehouse, to consolidated platforms like BigQuery or Microsoft Fabric. The buying decision is mostly about how much integration work you want to own and what your data gravity already is.

• Storage and compute model. Lakehouse, warehouse, or hybrid; open table formats (Iceberg, Delta) versus proprietary storage.
• Transformation and orchestration. Built-in dbt-like tooling, scheduling, and data lineage versus stitched best-of-breed.
• Serving surface. SQL only, plus notebooks, plus reverse-ETL, plus ML serving. Each surface affects which teams can self-serve.
• Per-org pricing axis and cloud gravity. Per-credit, per-byte, per-DBU, plus storage and egress. Same workload prices very differently depending on which vendor and which cloud.

The approach

Trial against your real top-10 use cases on real data. The vendor that handles your messy joins and existing pipelines wins, regardless of the slide deck.

• Use-case inventory. List the workloads (BI, ML training, reverse-ETL, exploratory analytics) and score each vendor against them.
• Open-format check. Iceberg or Delta storage keeps the door open if you change platforms; closed formats become lock-in.
• Total cost of ownership model. Storage, compute, transformation runtime, seat licences, and egress for a 12-month projection.
• Document the choice and the exit ramp. Capture rationale and how data and pipelines would migrate if pricing or product changed.

Why this compounds

The right data platform keeps paying back: pipelines stay reliable, dashboards stay fast, and analytics stops being the team that waits on infrastructure tickets.

• Operational consolidation. One platform per data class removes integration drift and shrinks the on-call surface.
• Cost discipline at scale. The right pricing axis for your volume saves more than negotiating discounts later.
• Faster decision cycles. Self-service analytics on a stable platform changes how often product asks data questions.
• Decision trail for the next renewal. The trial data becomes the renewal scorecard, not a cold start.