Feature Store Setup (Feast, Tecton) for Feature Management

Setting up Feature Store (Feast, Tecton) for feature management

The Feature Store solves one of the most pressing problems in production ML: the desynchronization of features between training and inference (training-serving skew). When a data engineer calculates a feature one way in Jupyter, and a backend developer calculates a feature another way in production code, the model degrades silently, and the cause is extremely difficult to pinpoint.

Feature Store Architectural Components

Any Feature Store consists of two repositories:

Offline store — for training models. It stores historical feature values with timestamps. Typically, these are BigQuery, Redshift, Snowflake, or Parquet files in S3. It supports point-in-time correct joins — this is critical to prevent future data leakage when preparing the training set.

Online store — for real-time inference. Stores only the latest feature values with a query latency of <10ms. Redis, DynamoDB, Cassandra, or Bigtable are used.

Feast: open-source version

Feast is the most widely used open-source Feature Store. Configuration includes:

1. Defining data sources (BigQuery table, Kafka topic, files)
2. Description of Feature Views in Python code:

from feast import FeatureView, Field, FileSource
from feast.types import Float64, Int64

user_stats = FeatureView(
    name="user_stats",
    entities=["user_id"],
    ttl=timedelta(days=7),
    schema=[
        Field(name="purchase_count_7d", dtype=Int64),
        Field(name="avg_order_value", dtype=Float64),
        Field(name="days_since_last_purchase", dtype=Int64),
    ],
    source=FileSource(path="s3://bucket/user_stats.parquet"),
)

3. Setting up materialization—the scheduled synchronization process between offline and online stores
4. Integrating the SDK into the training and inference code

The materialization schedule is configured via Airflow, Prefect, or the built-in scheduler:

feast materialize-incremental $(date +%Y-%m-%dT%H:%M:%S)

Tecton: enterprise version

Tecton provides a managed Feature Store with additional capabilities:

• Streaming features — calculation of features from Kafka/Kinesis in real time with latency <100ms
• On-demand features - calculation of features at the time of a request based on the context (for example, features that depend on the user's current request)
• Automatic monitoring of drift signs
• Feature lineage - tracking which models use which features

A typical Tecton use case is a bank where fraud scoring indicators need to be calculated based on the last 5 minutes of transactions in real time.

Implementation process

Post-implementation metrics

• Training-serving skew: reduced to zero for migrated traits
• Time to prepare a new training sample: from several hours to 5-15 minutes
• Reuse of features between teams: 40-60% of features of new models are already in the store
• Latency of obtaining features for inference: p99 < 10 ms when using Redis online store

Choosing between Feast and Tecton

Feast is suitable for teams with their own infrastructure, a DevOps budget, and agility requirements. Tecton is for enterprises where streaming feature support and a ready-made SLA are critical. Feature Stores also exist as part of cloud platforms: Vertex AI Feature Store (GCP), SageMaker Feature Store (AWS), and Databricks Feature Engineering.