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Version: 1.2

Glossary

A​

Aggregation Engine​

A distributed computation framework built into Tecton that enables efficient creation and serving of time-windowed aggregation features. It handles complex calculations over time windows while ensuring online/offline consistency and optimizing for performance.
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Attribute Feature​

A simple feature type in Tecton that represents a direct value from a column in transformed data. Attribute features are used for storing and serving individual data points like user properties or metadata.
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B​

Backfill​

The process of computing historical feature values for a feature view, ensuring data completeness before serving features in production. This is typically done in batch to populate past timestamps.
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Batch Feature View​

A type of Feature View that transforms data from batch sources (like data warehouses or data lakes) on a schedule. Batch Feature Views pre-compute feature values and store them in Tecton's feature stores for later retrieval.
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Batch Source​

A Data Source that specifies how to connect to and read from a batch data repository like Snowflake, BigQuery, S3, or a Hive table.
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Batch Schedule​

A configuration parameter that defines how frequently a Batch Feature View or Stream Feature View's offline materialization jobs run, typically specified as a time interval (e.g., timedelta(days=1)).
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C​

Cache​

A temporary storage layer that speeds up feature retrieval by storing recently accessed data, reducing the need for repeated computations or database queries. In Tecton, caching improves low-latency access to online features.
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Cluster​

A group of computing resources (e.g., Spark, Databricks, or Kubernetes nodes) used to process, store, or serve feature data efficiently.

Compaction​

A process that optimizes stored feature data by reducing redundancy and merging smaller data segments, improving performance and reducing storage costs.
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Compute Engine​

The processing system responsible for executing feature transformations and aggregations. This can include batch processing frameworks (e.g., Spark, Rift) or real-time streaming engines, depending on the feature pipeline requirements.
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Control Plane​

The component of a system responsible for managing configurations, orchestration, and metadata, ensuring proper coordination of data and compute operations. In Tecton, the control plane defines feature views, materialization schedules, and infrastructure settings.
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D​

Data Plane​

The layer responsible for executing data processing tasks, including feature transformation, storage, and retrieval. It handles the movement and computation of data based on configurations from the control plane.
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Data Source​

An object that defines how Tecton connects to and reads from external data systems. Data Sources abstract away connection details and ensure consistent interpretation of data both online and offline.
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E​

Embedding​

A vector representation of data (text, images, etc.) that captures semantic meaning in a form usable by machine learning models. In Tecton, embeddings are used particularly for GenAI applications to enable semantic search, clustering, and other similarity-based operations.
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Entity​

Represents a business object (e.g., user, product, transaction) that features describe. Entities serve as primary keys for feature lookups and are used to join together data from different sources.
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Environment​

A configuration that defines the runtime context for executing feature transformations, including Python packages and dependencies. Environments can be Tecton-managed (pre-configured) or custom-defined to support specific feature computation requirements.
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F​

Feature​

A measurable property or attribute of data used as input for machine learning models. Features are derived from raw data and can be transformed, aggregated, or stored for inference.
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Feature Server Group​

A cluster of live serving nodes capable of autoscaling that return feature vectors via Tecton's HTTP API. Feature Server Groups provide isolation at the workspace level, preventing resource contention between different teams' workloads and enabling granular resource management and cost attribution.
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Feature Table​

A structured storage format that holds computed feature values, indexed by entity keys and timestamps. Feature tables enable efficient retrieval of feature data for training and inference.
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Feature View​

The core building block in Tecton that defines transformations to convert raw data into features. Feature Views encapsulate logic for computing features consistently in both online and offline environments.
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Freshness​

The time delay between when raw data is generated and when a corresponding feature is available for model inference. Lower freshness latency improves real-time predictions.
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M​

Model​

A machine learning algorithm trained on features to make predictions or classifications based on new input data.
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P​

Pipeline​

A sequence of processes that transform raw data into features, including ingestion, transformation, materialization, and serving for model training or inference.
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S​

Skew/Drift​

The deviation between training and inference feature distributions (skew) or gradual changes in data patterns over time (drift), which can degrade model performance.
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Streaming​

A data processing paradigm where data is continuously ingested and transformed in real-time, enabling low-latency feature updates for online predictions.
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T​

Tile​

A precomputed, time-bucketed feature aggregation unit used to optimize storage and retrieval efficiency in Tecton's feature computation framework.
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Time Travel​

The ability to query historical feature values at specific timestamps, allowing models to reconstruct past feature states for training and debugging.
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Transform Server Group​

A group of Transform Server nodes that execute real-time transformations for Realtime Feature Views. Transform Server Groups support both provisioned and autoscaling modes, providing isolation between workloads to prevent resource contention and enable granular resource management.
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Training​

The process of feeding historical feature data into a machine learning model to learn patterns and optimize predictive accuracy.
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TTL (Time-to-Live)​

A retention policy that defines how long feature data is stored before being automatically deleted, balancing storage costs and data relevance.
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W​

Workspace​

A dedicated environment for managing and deploying Tecton feature pipelines. Workspaces provide isolation between different stages of development (e.g., dev, test, prod) or between teams sharing a Tecton deployment.
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