![QB-logo-h-fullcolor 1 [Vectorized]](https://www.quilt.bio/hs-fs/hubfs/QB-logo-h-fullcolor%201%20%5BVectorized%5D.png?width=1440&height=301&name=QB-logo-h-fullcolor%201%20%5BVectorized%5D.png "QB-logo-h-fullcolor 1 [Vectorized]"){kind=logo}
Use Databricks or Snowflake for
Large-scale structured analytics, ML compute, and BI dashboards. When the data is already tabular and trusted, they are excellent engines for it.
Quilt vs Databricks & Snowflake
How does Quilt compare to Databricks and Snowflake?
TL;DR
Quilt sits next to them. Databricks and Snowflake are compute and warehouse platforms for structured query and analytics. Quilt is the scientific data layer on S3 — file-and-package versioning for instrument and pipeline data — that feeds them. Most teams that use Quilt use at least one of them too.
Side by side
| Capability | Quilt | Databricks / Snowflake |
|---|---|---|
| Primary role | Scientific data layer — versioned files and packages on your S3 | Compute and warehouse platforms for structured analytics |
| Data model | Files + packages (FASTQ, BAM, imaging, tabular) with metadata | Tables, dataframes, and SQL workloads |
| Raw instrument data | First-class — versioned packages straight from instruments | Requires ETL into structured tables first |
| Versioning | Every package version preserved; results reproducible | Table-level features; not designed for raw file versioning |
| Lineage | Instrument-to-analysis lineage across file data | Job- and table-level lineage |
| Where data lives | Your S3 buckets, in your AWS account | Warehouse or lakehouse storage layers |
| Query | AI-powered search; SQL via AWS Athena; SDKs and APIs | Native SQL and Spark at massive scale |
| Governance of raw files | Audit trails, version history, and access control on file data | Strong governance for warehouse objects |
When to use which
Use Quilt for
Everything upstream of the warehouse: versioning and governing raw scientific files, search across every bucket, lineage from instrument to analysis, and reproducibility for regulated workflows.
The data layer that feeds your warehouse
Quilt makes the file layer trustworthy; your warehouse consumes it. Governed Quilt packages flow directly to Snowflake, Databricks, or custom pipelines — no manual exports, no CSVs. Because every package is versioned in your S3, analytics teams always know exactly which data produced which result.
- Governed data flows directly to Snowflake, Databricks, or custom pipelines
- SQL access to package data via AWS Athena
- Version-pinned inputs make warehouse results reproducible
- Everything stays in your AWS account
See it in action
Your S3 data, browsable with full context
Unlike warehouse-only views, Quilt packages combine files, metadata, and previews — in the AWS account where your scientific data already lives.
02 Package catalog
Browse your data like objects in S3 — with context
Every package is a self-contained unit: data, a README, rich metadata, and previews. Explore the tree, read the docs, and see exactly what's inside before you pull a byte.
- File tree, README, and key–value metadata in one view
- In-browser previews for images, tables, and notebooks
- Backed by your own S3 — data never moves
liver-rnaseq / cohort-2024
data/
counts.parquet
samples.csv
README.md
qc/
quilt_summarize.json
# Liver RNA-seq — Cohort 2024
Bulk RNA-seq across 1,284 hepatocyte samples. Aligned with STAR, quantified with Salmon, QC via MultiQC.
assayRNA-seq
organismH. sapiens
samples1,284
size42.7 GB
workflownf-core/rnaseq
qcpassed
Measured impact
Outcomes teams see with Quilt
90%
faster data lookup
Resilience
3×
NGS analysis throughput
Tessera
Weeks → minutes
from instrument to AI-ready package
30+
biotech & pharma teams
incl. Allen Institute, Inari
Trusted by leading life-sciences organizations
- Allen Institute
- Inari
- Flagship Pioneering
- Cellarity
- Resilience
- Tessera
Data lookups that used to take our scientists days now take minutes — with a single, governed source of truth the whole team can trust.
Frequently asked questions
Does Quilt replace Databricks or Snowflake?
No. Quilt handles file-and-package versioning for scientific data. Databricks and Snowflake handle structured query and analytics. Most teams that use Quilt use at least one of them too.
Can Quilt feed data into our warehouse?
Yes. Governed Quilt packages flow directly to Snowflake, Databricks, or custom pipelines — no manual exports, no CSVs.
Do we need a warehouse to use Quilt?
No. Quilt works standalone on your S3 buckets, with AI-powered search and SQL access via AWS Athena. A warehouse becomes useful when you need large-scale structured analytics.
Where does our data live?
In your own AWS account. Quilt deploys into your VPC and works with your existing S3 buckets — you keep control of the data, the security policies, and the costs.
Talk through your analytics stack
30 minutes with a Quilt engineer. Your architecture, your warehouse, and where a scientific data layer fits.