Implement ML-based features faster and with ease
A unified platform that brings ML functions directly into SQL, queries data where it lives, and accelerates your analytics with GPU computing.
SELECT campaign_name, budget, impressions, clicks, conversions, revenue
FROM postgres.campaigns
JOIN s3.impressions ON campaigns.id = impressions.campaign_id
JOIN clickhouse.metrics ON impressions.id = metrics.impression_id
WHERE ad_quality_score(impressions, clicks, conversions) > 0.7 -- ml in sql
AND fraud_score(clicks, conversions) < 0.3
AND predict_roi(budget, revenue) > 1.2
ORDER BY revenue DESC;Key Features
Enterprise-grade capabilities for modern data teams
ML in SQL
Faster time to market for ML-based functionality. Leverage ML models as SQL functions for faster, simpler analytics.
- MLOps out of the box
- Zero data transfer
- Models as SQL functions
Unified Compute
Support for both batch and stream compute paradigms for flexible data analytics.
- Batch on CPU/GPU
- Event streaming
- Change data capture(CDC)
GPU Acceleration
Leverage GPU computing to speed up complex analytics and AI/ML workloads.
- CUDA / OpenCL
- Parallel processing
- Hardware optimization
Advanced Querying
Query heterogeneous sources with multi-source JOIN operations and federated queries.
- Cross-database JOINs
- Federated queries
- Schema discovery
Fault Tolerance
Cross-database proxying with automatic node failure detection and dynamic load balancing.
- Auto-failover
- Load balancing
- Health monitoring
Extensibility
User-Defined Functions and Types for complex logic and custom objects.
- Custom UDFs
- Type extensions
- Extensible core
Where Otterstax Can Help
Heterogeneous Environments
S3, PostgreSQL, MySQL, ClickHouse, ScyllaDB - all in one query without data movement.
Mixed Workloads
Combine transactional, analytical, and ML workloads in a single query engine.
Long ETL/ELT Pipelines
Simplify or completely replace your data pipelines with federated queries.
Batch Analytics
Transform delayed analytics into real-time insights without infrastructure changes.
Step-by-Step ML Implementation
ML-in-SQL approach eliminates the gap between data and models.
Multiple Data Formats
Native support for Parquet, CSV, JSON, Arrow, ORC and other formats.
What is Otterstax
Three powerful concepts unified in one platform
Metadata-Aware Data Fabric
All your data is accessible in real time without centralized storage. Query data where it lives.
Intelligent Data Mesh
Domain teams can deliver data as a product with ease, maintaining ownership and autonomy.
Fast ML Implementation
ML models fed with real-time data provide accurate insights through SQL-native interfaces.
How Otterstax Works
ML Functions in SQL
Wrap ML functions into SQL to let them work with data directly - no typical ML infrastructure needed.
Query Data In Place
Query all organizational data where it is located. Only deltas are moved, not entire datasets.
GPU for AI/ML Workloads
Accelerate complex analytics with GPU computing for demanding AI/ML workloads.
Case Studies
Real-world results from companies using Otterstax
Real-Time A/B Testing Analytics
Marketplace
Real-Time Fraud Prevention
Trading Company
Cheaper Infrastructure for Fintech
Payment Company
Helping Retail Scale and Grow
E-commerce Platform