AI Data Platform
Unified Compute
Single processing framework for batch, streaming, and real-time data
Process data consistently across batch, streaming, and real-time sources
Handle batch, streaming, and real-time data within a single framework. Ensure consistency in feature computation across environments and use cases, eliminating discrepancies and simplifying data architecture. The framework facilitates efficient data flow from raw inputs to AI-ready context, regardless of data origin or velocity.
Handle diverse compute resources effortlessly
Tecton’s centralized orchestration layer manages compute resources across various engines. Optimizes resource allocation for performance and cost whether using Spark, EMR, Rift, BigQuery, or Snowflake. The system allocates necessary resources to data pipelines, reducing costs and streamlining AI application development.
Optimize feature vector computation with intelligent caching
Tecton’s advanced caching solution goes beyond simply reducing costs for individual feature calculations. It intelligently groups features with shared keys, minimizing redundant calculations and drastically reducing overhead to calculate the final feature vector—even when processing thousands of features simultaneously.
Leverage your existing computing infrastructure
Tecton works with existing compute infrastructure, integrating seamlessly with popular systems like Spark, EMR, and Snowflake. Push down compute while benefiting from Tecton’s advanced orchestration capabilities. This approach reduces disruption and accelerates feature adoption, ensuring smooth transition and optimal use of current resources.
Ready out-of-the-box computing with Rift
Rift, Tecton’s purpose-built compute engine for AI context creation, offers out-of-the-box data processing capabilities. It simplifies infrastructure by reducing third-party system dependencies. For large-scale distributed processing, Tecton integrates with Spark and Snowflake, providing flexibility and scalability.
Leverage specialized feature processing engines
Tecton’s optimized engines handle common, complex data processing tasks. The time-window aggregation engine computes temporal features, while the embeddings engine generates vector representations. These engines improve performance, reduce processing costs, and streamline feature engineering workflows for sophisticated feature creation.
