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# Query Petabytes like it's Terabytes

Self-optimizing, lossless, state-of-the-art compression that turns petabytes into terabytes. Halve spend, double speed across Iceberg, Delta, Trino, Spark, Snowflake, Databricks and beyond.

BOOK A DEMO

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Cost Savings DemoQuery Performance Demo

The above demo showcases Databricks, but Granica works seamlessly across Iceberg, Trino, Spark, Snowflake, BigQuery and more.

Book a live demo on your lake →

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## Trusted by Data + AI Leaders Across the Globe

See how top brands trim data bloat, speed queries, and free engineers to focus on new features.

Global Revenue-Intelligence SaaS

> “Crunch halved our 20 PB data lake without a single pipeline change — this is magical.”

VP, Data Engineering

60%

less storage — Hive on AWS

$5M+

annual ROI

CONSUMER SOCIAL-MEDIA UNICORN

50%

storage saved — Delta Lake on GCP

2x

faster and lower cost than Databricks' built-in Optimize feature

LEADING SOCIAL MEDIA COMPANY

$20M+

annual ROI — Hive/Iceberg on AWS

3x

less developer time on data-lake optimization

DIGITAL EXPERIENCE ANALYTICS SAAS

3x

lower TCO for data platform

$3M+

annual ROI

FORTUNE 500 HEALTHCARE PROVIDER

50%

less storage — BigQuery/Iceberg on GCP

2x

lower data transfer costs

## Compress without limits, spend nothing

Self-optimizing, lossless compression that shrinks storage to pennies and supercharges every model with instant data access.

### Any Lake

Works with Iceberg, Delta, Trino, Spark, Snowflake, BigQuery, Databricks, and more—zero disruption.

### Petabytes to exabytes

Throughput climbs, latency falls as data grows.

### Pays for itself

Storage shrinks, compute drops, pipelines fly—ROI in days.

BOOK A DEMO

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## Built for structure, optimized for AI

Everything you need to run structured AI that just works, forever.

### Native & Transparent

Deploy inside your VPC. Zero code, zero downtime.

### Continuously Adaptive

Learns every query and data pattern, reshapes compression on the fly.

### Hands-off Orchestration

Set a cost-performance target once. Granica auto-scales forever.

### Trusted Controls

SOC-2 Type 2, full audit logs, nothing leaves your cloud.

### Lineage on Tap

Pipe immutable logs to SIEM, finance, and compliance.

### Day-zero Activation

One call. Dashboards show $-savings and performance gains before coffee cools.

VIEW DOCS

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## Proven performance at scale

Real-world results from petabyte-scale deployments

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Compression RatioCost Savings vs Data VolumeQuery Performance vs Complexity

Scatter plot showing compression ratio vs query cost reduction.0255075100Compression Ratio (%)010203040Query Cost Reduction (%)BestStructuredAverage

Dataset Type (sample)

Compression Ratio (%)

Query Cost Reduction (%)

Best – highly compressible high cardinality data

~80%

35%

Structured – enterprise logs, events & lookups

~60%

25%

Average – Large fact & mixed workloads

~40%

15%

Best – highly compressible high cardinality data

Compression Ratio (%)

~80%

Query Cost Reduction (%)

35%

Structured – enterprise logs, events & lookups

Compression Ratio (%)

~60%

Query Cost Reduction (%)

25%

Average – Large fact & mixed workloads

Compression Ratio (%)

~40%

Query Cost Reduction (%)

15%

#### Shrink data, shrink bills with SOTA compression

Granica's entropy-aware compression strips out 45–80% of bytes, slicing cloud query spend 15–35% across every workload class.

##### Methodology

Directional averages blend TPC-DS benchmarks with anonymized telemetry from production clusters (1–100 PB).

##### Validated by

Dozens of SaaS, consumer-internet, healthcare and transportation deployments ranging from 1 PB to 100+ PB.

## AIA self-improving data factory, for

We're building a new class of data infrastructure for AI. Turn any lake into a self-optimizing data factory—compression today, advanced subsampling and safe synthetic data tomorrow.

START BUILDING

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Fundamental research

## Turning entropy to intelligence

Granica is advancing the state-of-the-art in data for AI. Turning exabyte-scale noise into real-time reasoning. Shifting the world from ETL to E∑L.

EXPLORE RESEARCH

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### Scaling laws for learning with real and surrogate data

Collecting large quantities of high-quality data can be prohibitively expensive or impractical, and a bottleneck in machine learning. We introduce a weighted empirical risk minimization (ERM) approach for integrating augmented or 'surrogate' data into training.

Read paper

NeurIPS 2024

### Towards a statistical theory of data selection under weak supervision

Given a sample of size N, it is often useful to select a subsample of smaller size n<N to be used for statistical estimation or learning. Such a data selection step is useful to reduce the requirements of data labeling and the computational complexity of learning.

Read paper

ICLR 2024 Best Paper (Honorable Mention)

### Compressing Tabular Data via Latent Variable Estimation

Data used for analytics and machine learning often take the form of tables with categorical entries. We introduce a family of lossless compression algorithms for such data.

Read paper

ICML 2023

## FAQs

Get answers to common questions about Granica Crunch, our advanced compression system for AI and analytics workloads.

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### 01

### What is Granica Crunch?

### 02

### How does Crunch integrate with my data stack?

### 03

### Will Crunch speed up performance?

### 04

### How is Crunch priced?

### 05

### Is Crunch secure and compliant?

BOOK A DEMO

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©2025 Granica Computing, Inc.

©2025 Granica Computing, Inc.

Granica | Query Petabytes like it's Terabytes