Introducing CloudQuant Data Liberator

The Data Problem Every Organization Faces

Your data is everywhere. Databases. Spreadsheets. Cloud storage. SaaS applications. Legacy systems. And traditional data warehouses tell you there's only one solution: move it all into one place.

So you build ETL pipelines. You transform data into rigid schemas. You maintain jobs that break when source systems change. You spend months getting data in, then more months keeping it flowing.

And you realize: you're spending more time moving data than using it.

The False Choice

Traditional data warehouses force you to choose:

Option 1: Consolidate everything

• Lose control of your data
• Deal with stale copies
• Maintain brittle ETL pipelines
• Pay for massive storage

Option 2: Accept fragmentation

• Data silos everywhere
• Manual data gathering
• No cross-system analysis
• Tribal knowledge required

But what if there was a third option? What if you could query data where it lives?

Enter Data Liberator

Data Liberator is a data virtualization platform that creates a unified query layer over your existing data sources. No ETL. No data movement. No schema migrations.

Connect Data Liberator to:

• RDBMS databases (PostgreSQL, Oracle, SQL Server, MySQL, TimescaleDB)
• File systems (local, network, S3, Azure Blob, Google Cloud Storage)
• APIs (REST, GraphQL, custom protocols)
• Legacy systems and proprietary formats

Don't see your data source? We build custom connectors. If your data exists somewhere, Data Liberator can reach it.

How It Works

1. Connections

Point Data Liberator at your data sources. Each connection knows how to talk to its system—database credentials, file paths, API endpoints. Your data stays where it is.

2. Datasets

Define what data you want to expose. A dataset might be:

• A database table, view, or query
• Files matching a pattern (e.g., sales_report_YYYYMMDD.xlsx)
• An API endpoint response

Data Liberator automatically understands the structure—RDBMS tables, CSV files, JSON from APIs, Parquet, Arrow, whatever format you have.

3. Descriptions (The Secret Sauce)

During onboarding, subject matter experts add descriptions to datasets and columns. Not generic metadata—context that makes sense to your organization:

• "Daily equity pricing from our primary vendor, updated 6 AM ET"
• "adj_close = closing price adjusted for splits and dividends"
• "Updated nightly from vendor SFTP drop"

This context becomes immediately available to everyone—new team members, AI agents, cross-department analysts. The knowledge lives with the data.

4. Query Through One Interface

Now all your data is accessible through a single RESTful API. Query across systems. Join data from different sources. Get fresh results without waiting for ETL jobs.

Real-World Example

Most businesses generate weekly reports saved as spreadsheets in shared drives:

The old way:

• Build ETL to extract data from each file
• Transform into common schema
• Load into warehouse
• Maintain pipeline when formats change
• Wait for scheduled loads

The Data Liberator way:

• Point to the directory
• Define the file pattern
• Query the data

When new files arrive, they're instantly queryable. No pipeline, no wait, no maintenance.

Who Needs This?

Any organization that:

• Has data scattered across multiple systems
• Needs to analyze data together without consolidating it
• Maintains brittle ETL pipelines
• Struggles with data silos
• Values data sovereignty and security

We've seen this work across manufacturing, healthcare, energy, retail, finance, and telecommunications. If you have distributed data, Liberator can free it. Contact us for a demo today.