Chalk for Data Engineers

Chalk unifies your data schema to compute and serve features defined in Python consistently across batch, training, and real time.

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Trusted by teams building the next generation of AI + ML

Why data engineers choose Chalk

Eliminate manual pipelines

Chalk automatically builds and executes DAGs for computing your queries, removing custom orchestration

Unified feature catalog

Ensure consistent feature definitions across backfills, training sets, and real-time inference from a single source of truth

Simplify temporal aggregations

Define rolling windows, decays, and normalized features directly in Python

Serve in real time without extra systems

Low-latency APIs without Kafka, Flink, or custom streaming stacks

Generate reproducible training data

Create point-in-time-correct datasets with full lineage and versioning

Eliminate manual pipelines

Chalk automatically builds and executes DAGs for computing your queries, removing custom orchestration

Unified feature catalog

Ensure consistent feature definitions across backfills, training sets, and real-time inference from a single source of truth

Simplify temporal aggregations

Define rolling windows, decays, and normalized features directly in Python

Serve in real time without extra systems

Low-latency APIs without Kafka, Flink, or custom streaming stacks

Generate reproducible training data

Create point-in-time-correct datasets with full lineage and versioning

Eliminate manual pipelines

Chalk automatically builds and executes DAGs for computing your queries, removing custom orchestration

Unified feature catalog

Ensure consistent feature definitions across backfills, training sets, and real-time inference from a single source of truth

Simplify temporal aggregations

Define rolling windows, decays, and normalized features directly in Python

Serve in real time without extra systems

Low-latency APIs without Kafka, Flink, or custom streaming stacks

Generate reproducible training data

Create point-in-time-correct datasets with full lineage and versioning

Define once, use everywhere

@online
def get_quote_is_risky(
    owned_vehicles_count: Quote.owner.owned_vehicles_count,
    n_addresses_30d: Quote.owner.n_addresses["30d"],
    n_addresses_1yr: Quote.owner.n_addresses["365d"],
) -> Quote.is_risky:
    return (
        n_addresses_30d > 1 or n_addresses_1yr > 5
    ) and owner_owned_vehicles_count > 2

Define once, use everywhere

Chalk isn’t just a feature store. It’s an execution graph for your features. At request time, Chalk slices the graph and computes only what’s needed, from the freshest data available.

EXPLORE ONLINE QUERIES

Instead of relying on stored data to stay in sync, we compute directly from the source. Chalk makes that both precise and reliable.

Robert Theed Backend Tech Lead, iwoca

Explore how Chalk works

engineering

Accelerating Real-Time ML with Symbolic Python Interpreter

2025.04.01

product

Chalk for Data Engineers

2025.08.20

customer story

Medely staffs critical healthcare in real-time with Chalk

2025.12.11

product

Quarterly Product Update: Fall

2025.11.10

engineering

Accelerating Real-Time ML with Symbolic Python Interpreter

2025.04.01

product

Chalk for Data Engineers

2025.08.20

customer story

Medely staffs critical healthcare in real-time with Chalk

2025.12.11

product

Quarterly Product Update: Fall

2025.11.10

engineering

Accelerating Real-Time ML with Symbolic Python Interpreter

2025.04.01

product

Chalk for Data Engineers

2025.08.20

customer story

Medely staffs critical healthcare in real-time with Chalk

2025.12.11

product

Quarterly Product Update: Fall

2025.11.10

Ready to ship next‑gen ML?

Talk to an engineer and see how Chalk can power your production AI and ML systems.

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