November 03, 2025
Choosing a “best” data warehouse in 2026 depends on your workloads, team skills, and budget discipline. Here’s a practical, vendor-balanced guide to Snowflake, Databricks, and DuckDB—what they’re good at, where they struggle, and how we help teams decide fast and with data.
What “best” means in 2026
Focus on the shape of your work, not the brand:
- Workloads: governed BI, ad‑hoc analytics, ML/AI, streaming, or ELT/ETL.
- Team: SQL‑first analysts vs. data engineers and ML practitioners.
- Scale & concurrency: dashboard peaks, long‑running jobs, and SLAs.
- Cost control: elastic scale, idle posture, finops guardrails.
- Governance: lineage, PII controls, audit, multi‑region requirements.
A quick scoping call and a 1‑week bake‑off usually reveal the right answer. Most of the time you need a combination of all the points.
Quick note
There are no wrong choices. Most data warehouses simply work; that’s why you need to evaluate each of them to find which underlying features you need and want.
Some key features
- Snowflake (cloud warehouse): compute “warehouses” scale independently; tasks/streams for ELT; rich governance and ecosystem (marketplace, Snowpark).
- Databricks (lakehouse): object storage + Delta Lake, notebooks + SQL Warehouses, ML runtime, Unity Catalog for governance.
- DuckDB (in‑process OLAP): embedded analytics engine, great on Parquet/CSV/JSON; no infra to run; pair with Airflow/Marimo notebooks and Superset for dashboards; MotherDuck for hosted multi‑user SQL.
Real‑world fits and example stacks
1) Governed BI at mid/large scale (low ops)
- Choose: Snowflake
- Stack: Fivetran/Airbyte → Snowflake → dbt → Superset/Looker.
- Why: excellent isolation/concurrency, predictable operations, mature RBAC and data sharing.
2) Data + AI on open data (batch + streaming)
- Choose: Databricks
- Stack: Kafka/Kinesis → S3/ADLS + Delta Lake → Databricks (DLT, notebooks, SQL) → Lakehouse dashboards.
- Why: first‑class ML/AI and streaming, strong open‑format story, robust governance with Unity Catalog.
3) Cost‑sensitive analytics and ELT without infra
- Choose: DuckDB (optionally MotherDuck)
- Stack: DLT → Parquet on object storage + DuckDB → Superset/Marimo.
- Why: near‑zero infra, fast local dev, great for SMBs and departmental analytics.
Trade‑offs at a glance
| Criteria | Snowflake | Databricks | DuckDB |
|---|---|---|---|
| Governance & RBAC | Mature, built‑in | Unity Catalog strong | Minimal (use platform/tooling) |
| SQL UX for BI | Excellent | Good, improving | Excellent locally; limited multi‑user |
| ML/AI tooling | Good via Snowpark | First‑class | External Python/R notebooks |
| Streaming | Basic patterns | Strong (Structured Streaming/DLT) | External tooling |
| Open formats (Parquet/Delta) | Indirect | Native (Delta Lake) | Native (Parquet/CSV/JSON) |
| Cost control posture | Elastic; watch idle/credits | Elastic; watch cluster/DBU | Minimal infra; watch concurrency |
| Concurrency at scale | Strong isolation | Strong with SQL Warehouses | Single‑node; MotherDuck for shared |
Notes:
- MotherDuck adds hosted SQL, permissions, and collaboration on DuckDB with minimal ops.
- “Cost control” is more about your habits (idle policies, permissions, quotas) than the vendor.
Questions to ask yourselves
Use this cheatsheet to ask yourselves the right questions:
Workloads and SLAs:
- BI dashboards peak QPS (query per second)?
- Batch sizes/runtime and deadlines?
- Streaming latency and uptime needs?
- What is the impact of an outage?
Volumetry
- What is the volumetry of my data?
- How fast is it growing?
- In a few years, what would be the scale of my data?
- Will the solution work for 10x my current scale? see this article
| Annual growth | Years to hit 10Ă— scale |
|---|---|
| 10% | 24.16 y |
| 25% | 10.32 y |
| 50% | 5.68 y |
| 75% | 4.11 y |
| 100% | 3.32 y |
| 150% | 2.51 y |
| 200% | 2.10 y |
Data footprint and formats
- Today: Parquet/CSV, Delta/Iceberg, or proprietary?
- Tomorrow: do you need open interchange between tools/clouds?
Team profile
- Mostly SQL analysts or code‑first engineers/ML?
- Comfort with notebooks vs. SQL editors vs. drag‑and‑drop tools?
- Tech culture: R, SQL, Python, Spark?
Guardrails and cost posture
- Default idle/auto‑suspend, quotas, and tags for chargeback.
- Define “good enough” performance so you don’t over‑provision.
- Do you have FinOps expertise?