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See how top brands trim data bloat, speed queries, and free engineers to focus on new features.
“Crunch halved our 20 PB data lake without a single pipeline change — this is magical.”
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.
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.
| 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% |
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.
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.
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.
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.