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Data Packages

Transform Data Chaos into Reusable Assets

Flexible, interoperable data packages that compound value over time

Give teams the power to define their own processes, use their own datatypes, and innovate as quickly as they want. Gain organizational-wide data findability while building reliable, trustable, version-controlled data products.

What's Data Chaos?
Quilt Open Source
ccle-test-3/SRR8788981
@ 2f07364e26
📋 METADATA
study: "CCLE"
cancer_type: "Lung Cancer"
organism: "Homo sapiens"
data_type: "WGS"
Showing 1—5 out of 5
📄 SRR8788981.runinfo_ftp.tsv 1.6 kB
📄 SRX5578768_SRR8788981_1.fastq.gz 66.5 kB
📄 SRX5578768_SRR8788981_2.fastq.gz 66.2 kB
Data bundled with metadata and context
2X
More Valuable

INFINITE POTENTIAL: THE DATA REVOLUTION

Today, however, the industry is changing. AI and Machine Learning are creating new opportunities to answer much broader questions than lab data was originally intended for. In fact, they require this data to train models that will rise above the pack. So leveraging data beyond its original scope is no longer just a nice to have - To be competitive in biotech, it’s increasingly an imperative.

A PARADIGM SHIFT

Biotechs of the past treated data as a resource that they used once and then threw away. They were effectively burning it like oil. The successful biotechs of the future will treat data as an asset that continues to compound value the longer they keep it. But like any asset, this can only happen if they invest in maintaining it.

DATA: REIMAGINED

Data bundled with metadata is doubly as valuable, allowing teams to trust, find and reuse data to create multiple successful outcomes. With metadata, organizations can make better, faster decisions, and rely on their data assets to lead the way.

If you can't find the data, you can't reproduce the analysis.

What Are Data Packages?

Data packages are intelligent manifests that combine pointers to data (like objects in Amazon S3) with rich context about that data, including lineage, metadata, and revision history.

If you can't find the data, you can't reproduce the analysis.

Data without context is not reusable. Traditional file storage separates data from its meaning, making collaboration and discovery nearly impossible.

🔗

Intelligent Manifests

Combine pointers to data with rich context and lineage

📋

Self-Contained

Everything needed to understand and reproduce the analysis

🔄

Versioned

Track every change with cryptographic integrity

🔍

Discoverable

Find and access data through metadata and search

What Makes Data Packages Powerful

Data packages combine the best of traditional data management with modern cloud-native capabilities, creating a foundation for reliable, discoverable, and reusable data assets.

📦

Data Packaging

Create versioned packages with metadata using the free Python SDK

Open Source - Free
Open Source
📦

Rich Visualizations

Document previews, dashboards

Rich Visualizations
Platform
📦

Powerful Search

Metadata-driven discovery

Find Everything
Platform
📦

Team Collaboration

Web interface, permissions

Open Source - Free
Platform

Built for Your Infrastructure

Data packages integrate seamlessly with your existing cloud infrastructure and analysis platforms, providing a vendor-neutral foundation for your data assets.

Amazon Web Services

Native S3 integration with advanced AWS technology partnership

Integration

Amazon Web Services

Native S3 integration with advanced AWS technology partnership

Integration

Amazon Web Services

Native S3 integration with advanced AWS technology partnership

Integration

The Data Package Lifecycle

From creation to collaboration

Data packages follow a simple yet powerful workflow. Start with the free Python SDK for basic packaging, or use the full platform for advanced collaboration.

1
📦

Create

Bundle data with metadata (SDK) or use web interface (Platform)

→
2
🔄

Version

Track changes with SHA-256 checksums

→
3
🤝

Share

Collaborate across teams and platforms

→
4
🔍

Discover

Access via SDK commands or rich web search (Platform)

Starter

$0 /month

Perfect for individual researchers

✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}

Starter

$0 /month

Perfect for individual researchers

✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}

Starter

$0 /month

Perfect for individual researchers

✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}
✓ {feature_text=Up to 10GB storage}

Real-World Applications

See how teams across biotech and life sciences use data packages to accelerate discovery and ensure reproducibility.

🧬

Genomics Research

Package sequencing data with sample metadata for reproducible analysis pipelines

Key Benefit: Reproducible Analysis
Example: FASTQ, VCF, BAM files + sample metadata
🧬

Genomics Research

Package sequencing data with sample metadata for reproducible analysis pipelines

Key Benefit: Reproducible Analysis
Example: FASTQ, VCF, BAM files + sample metadata
🧬

Genomics Research

Package sequencing data with sample metadata for reproducible analysis pipelines

Key Benefit: Reproducible Analysis
Example: FASTQ, VCF, BAM files + sample metadata
🧬

Genomics Research

Package sequencing data with sample metadata for reproducible analysis pipelines

Key Benefit: Reproducible Analysis
Example: FASTQ, VCF, BAM files + sample metadata
The Data Revolution

From Expendable Resource to Reusable Asset

AI and Machine Learning are creating new opportunities to answer much broader questions than lab data was originally intended for. To be competitive in biotech, leveraging data beyond its original scope is no longer just nice to have—it's an imperative.

🤖

AI/ML Ready

Data packages provide the structured, contextualized data that AI models need to excel

🤖

AI/ML Ready

Data packages provide the structured, contextualized data that AI models need to excel

🤖

AI/ML Ready

Data packages provide the structured, contextualized data that AI models need to excel

Asset 5

Why build data packages?

Data is more powerful with context

Reliable, accessible, and well-documented data is far more valuable than unorganized data. Organizations that effectively package their data realize twice the value of those that don't. With AI and Machine Learning expanding the potential uses of data, utilizing it in varied contexts to create value is essential. For biotech competitiveness, leveraging data beyond its initial intent has become crucial.
Data in packages are
2X
More Valuable
Unfortunately,

Data without context is not reusable

LINKED DATA IS REUSABLE DATA

The final step in moving from data as an expendable resource to data as a reusable asset is to begin linking data to other datasets and resources that will allow teams to recreate and understand the original context, and understand how the data can be leveraged in new ways.
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What are Data Packages

Data packages are searchable, sharable, versioned, reproducible, and self contained. In essence, data packages are intelligent manifests that combine a list of pointers to data (for example objects in Amazon S3 or files in Sharepoint), with context about those data, including lineage, metadata, and revision comments.

Not only do data packages keep track of their own versions, they keep track of the versions of underlying data as well, giving teams the ability to review every version of every document contained in a data package. Teams can grab a version of a package and run it through pipelines to test repeatability, or can link a historical package to their colleague to ensure they’re iterating on the same data.
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Data Packages Defined

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Data with context

Data packages offer a streamlined approach to data management by storing both data and metadata together in object storage. This integrated method contrasts with traditional practices where metadata and versioning information are stored separately in databases, while the actual data resides in object stores. By consolidating data and its contextual information within the same storage unit, data packages enhance the integrity and coherence of data management, ensuring that context and content are always aligned and readily accessible.

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Deeply Versioned

One of the significant advantages of data packages is their support for deep versioning, which is facilitated by the use of SHA-256 checksums for each revision. This cryptographic hash function ensures the integrity of data by providing a unique identifier for every version, allowing users to track changes, verify data accuracy, and revert to previous versions if necessary. This robust versioning capability enhances data reliability and facilitates meticulous data management across different iterations.

Flexible Metadata

Data packages excel in accommodating diverse metadata needs through their flexible metadata schema. Unlike rigid systems that enforce a single, uniform metadata schema across all datasets, data packages allow teams to capture and manage metadata in a way that best suits their specific requirements. This flexibility ensures that relevant details are preserved and easily accessible without the need for a one-size-fits-all approach, thus supporting a wide range of data use cases and applications.

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Interoperable Data

The interoperability of data packages is a crucial feature, as it allows seamless integration and sharing of data across different platforms. By storing data in an open-source, customer-owned data storage system, data packages enable the attachment of data from various platforms (Platform A and Platform B) while maintaining compatibility. This open approach ensures that data can be effectively utilized across diverse systems and environments, fostering greater collaboration and data exchange.

Easily Accessed

Data packages are designed to be easily accessible, with features like SQL querying and faceted search to enhance data retrieval. These functionalities allow teams to efficiently locate and extract the data packages they need, regardless of the cloud environment they are using. The ability to perform advanced searches and queries ensures that users can quickly find relevant datasets and integrate them into their workflows, significantly improving data accessibility and usability.

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Data packages aren't theoretical

Build data packages with Quilt TODAY

Quilt is making it easier for teams to amass, oversee, and access datasets enriched with comprehensive, accurate context. Start building powerful data packages today with Quilt..
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AWS

Quilt is an Advanced AWS Technology Partner

Quilt Data is an AWS Advanced Technology Partner. Quilt brings seamless collaboration to Amazon S3 by connecting people, pipelines, and machines using visual, verifiable, versioned data packages. Amazon Web Services provides secure, cost-effective, and scalable big data services that can help you build a Data Lake to collect, store, and analyze massive volumes of heterogeneous data.