DATA WITHOUT SILOS
Open analytics
for BI and AI.
Tarantula Lakehouse brings structured, semi-structured and unstructured data into an open architecture. Storage and compute scale independently, while sensitive data can remain on premises.
Tarantula Lakehouse integrates structured, semi-structured and unstructured data using open table formats. Separating storage and compute supports SQL analytics and AI/ML workloads together. Keep sensitive data in-house while scaling with data volumes and analytical demand.
WHY LAKEHOUSE
Combine the strengths of a data lake and warehouse in one operating model
Data integration
Bring business databases, logs, files, images and external data into one storage layer to reduce departmental silos.
- Store structured and unstructured data
High-performance SQL
Distributed SQL and columnar data structures support large-scale aggregations, joins and interactive analytics.
- Trino MPP SQL Engine
Storage efficiency
Object storage and compression reduce long-term retention costs and extend the useful life of data assets.
- ToS (Tarantula Object Storage)
Openness
Standard formats and interfaces establish a data foundation independent of a particular analytics tool or cloud.
- Iceberg open table format
AI Ready
Connect BI, data science, model training and RAG pipelines to the same data.
- 100% S3 API compatibility
Hybrid architecture
An on-premises foundation provides an expansion path to cloud analytics services when needed.
- Integration experience with AWS and Snowflake
FLOW DIAGRAM
From ingestion to analytics and AI
one connected data flow
Ingestion requires development tailored to each data source and is delivered with UNNET’s specialist partners.
POC BENCHMARK
Aggregation and join performance validated on large transaction datasets
A PoC measured aggregations and joins on structured datasets ranging from tens of millions to billions of records, demonstrating strong analytical performance against the comparison systems.
| Query workload | Comparison A | Comparison B | Tarantula Lakehouse |
|---|---|---|---|
| Daily aggregation (10 million records) | 8 s | 0.7 s | 0.4 s |
| Monthly aggregation (hundreds of millions of records) | 13 s | 1 s | 0.8 s |
| Annual aggregation (billions of records) | 26 s | 2 s | 4 s |
| Daily join across two tables | 12 s | 1.2 s | 1 s |
| Monthly join across two tables | 29 s | 3.3 s | 2.5 s |
| Annual join across two tables | 52 s | 15 s | 19 s |
CUSTOMER STORY / ALTERNATIVE TRADING SYSTEM
Turn rapidly growing transaction data into an asset
Data generated daily
Annual data growth
Deployed storage capacity
Production servers
Transaction data grew four to five times faster than initially projected. A PoC on hundreds of millions to billions of records validated long-term retention and analytics. After assessing performance, cost and usability, deployment began in November 2025 and the operational foundation was completed in approximately four months.
DEPLOYMENT
Start small and scale with your data.
Standard Edition
Minimum configuration
Designed for project research and development at hospitals, laboratories, local governments and small or medium-sized enterprises. Suitable for 500GB–10TB, it provides high-speed structured-data analytics and a repository for straightforward AI projects.
- Single compute configuration
- Community Version object storage
- 8×5 technical support / emergency response within one day
ENTERPRISE
Recommended configuration
An enterprise lakehouse for finance, manufacturing and retail. Suitable for 50TB and above, it provides high-speed warehouse-class structured-data analytics and a highly available platform for structured and unstructured AI workloads.
- Highly available compute with active-active expansion
- Enterprise Version object storage
- 24×7 technical support / emergency response within four hours
Licensing and server specifications are determined by retained data volume, daily ingestion, concurrent queries, retention period and required availability.
Predictable cost
Control infrastructure and processing costs with an on-premises-first model.
Open architecture
Use open table formats, catalog services and standard interfaces without lock-in.
AI ready
Serve the same governed data to BI, data science, model training and RAG.
LAKEHOUSE STACK