Selena Lakehouse — next-generation intelligent data analytics
for fast, scalable, and secure data storage and processing
The flexibility of a Data Lake and the reliability of a Data Warehouse
Intelligent analytics with AI & ML support
Scalability without overpaying for infrastructure
Fast data access without complex ETL
Why traditional data approaches no longer work?
Data duplication, chaos, and lack of control
Limited scalability and rising infrastructure costs
Difficult integration with BI, AI, and Data Science tools
Security risks and challenges in access management
Slow processing due to complex ETL workflows
Selena Lakehouse — a flexible, scalable, and secure platform
Versatility
support for structured and unstructured data (JSON, ORC, Parquet, CSV)
Security
centralized access control and data protection
Automated query management
intelligent SQL optimizer
Storage flexibility
deployable in the cloud, on-premises, or in a hybrid environment
High performance
MPP architecture, vectorization, and automatic caching
Analyze any data in real time without limitations
Selena Lakehouse combines the best of both worlds
The reliability and precision of a Data Warehouse
structured data, BI, high-speed analytics
The flexibility and scalability of a Data Lake
unstructured data, machine learning, cost-efficient storage
Advantages of Selena over other solutions
Selena
Use cases
Greenplum
ClickHouse
Real-time analysis of any data, building a data lakehouse
SIMD instruction execution
Predictive analytics, reporting, building DWH, data modeling
Fast data analysis in wide, flat tables (data marts)
No
Yes
Yes
Partial
Yes
Partial
Caching of queries, intermediate and final results
Support for complex joins and concurrent queries
High
Low
High
Low
High
Operating, maintenance, and scaling cost
High
How does it work?
Kodjin FHIR Profiler
seamless integration with analytics platforms and ML models
Massively parallel processing
high-speed, optimized SQL query execution
Connect to all data sources
databases, APIs, files, and Data Lakes
Store in open formats
supports Apache Iceberg, ORC, Parquet, and CSV