FlowDiagram
AI & ML pipelinesMermaid

Feature store architecture template

An offline and online feature store: batch and streaming pipelines compute features once, a registry defines them, and training and serving read consistent values — for ML platform teams. This data flow diagram template contains 16 labelled components, branches such as “Low-latency lookup”, “No”, “Yes”.

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Components in this data flow diagram

  • Application databases
  • Batch pipelines: Spark & dbt
  • Kafka event streams
  • Streaming features: Flink
  • Feature registry: definitions, owners, versions
  • Offline store: Snowflake / Delta Lake
  • Online store: Redis
  • Materialisation job
  • Point-in-time correct joins
  • Model training: PyTorch / XGBoost
  • Model registry
  • Online inference service
  • Fraud, pricing & personalisation apps
  • Freshness & drift checks pass?
  • Alert feature owner
  • Feature catalogue & discovery

How to use this data flow diagram template

  1. Click Open in the editor. The data flow diagram opens on a new page in FlowDiagram — nothing is uploaded and there is no sign-up.
  2. Rename, add or delete shapes and drag them where you want; connectors stay attached as things move.
  3. Drop in vendor logos from the library — 1,160 of them, including every AWS, Azure and Google Cloud service icon — or restyle it with the colour and theme controls.
  4. Prefer text? Edit the Mermaid source below and paste it in through ☰ → Import diagram (see the Mermaid editor); it imports as an editable diagram.
  5. Export to PNG, SVG or PDF, or press Ctrl+L for a link that carries the whole diagram.

Mermaid source

The whole template is the text below. Paste it into FlowDiagram, keep it in Git next to the code it describes, or hand it to an AI assistant to extend.

flowchart LR
  OLTP[(Application databases)] --> BATCH[Batch pipelines: Spark & dbt]
  EVENTS[Kafka event streams] --> STREAM[Streaming features: Flink]
  REG[Feature registry: definitions, owners, versions] -.-> BATCH
  REG -.-> STREAM
  BATCH --> OFFLINE[(Offline store: Snowflake / Delta Lake)]
  STREAM --> ONLINE[(Online store: Redis)]
  OFFLINE --> MAT[Materialisation job]
  MAT --> ONLINE
  OFFLINE --> PIT[Point-in-time correct joins]
  PIT --> TRAIN[Model training: PyTorch / XGBoost]
  TRAIN --> MODELREG[(Model registry)]
  MODELREG --> SERVE[Online inference service]
  ONLINE -->|Low-latency lookup| SERVE
  SERVE --> APPS[Fraud, pricing & personalisation apps]
  OFFLINE --> QUALITY{Freshness & drift checks pass?}
  QUALITY -->|No| ALERT[Alert feature owner]
  QUALITY -->|Yes| CATALOG[Feature catalogue & discovery]
  CATALOG --> REG

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