Get Started with Code Examples
Unlock the power of real-time data pipelines
Withdrawal Model
Decide and enforce withdrawal limits with custom hold times.
Income
Compute income from Plaid transactions.
Cache Busting
Bypass the cache with a max-staleness of 0.
Sensor Streams
Compute streaming window aggregate functions on sensor data.
Deploy with Chalk
Deploy to Chalk (either as a preview deployment or to production).
Changes in Behavior
Detect changes in user behavior over time.
Unit tests
Resolvers are just Python functions, so they are easy to unit test.
Sharing Resolvers
Resolvers are shared between all models.
Custom Feature Types
Downstream DataFrames
Chain a DataFrame resolver with a scalar resolver.
Failing Sensors
Combine batch, caching, and DataFrames to create a powerful predictive maintenance pipeline.
Mapping Stream
Create features directly from messages on a stream.
Pre-Fetching
Keep the cache warm by scheduling a resolver to run more frequently than the max-staleness.
Downstream Scalars
Resolvers chain together through their required dependencies and declared outputs.
Feature Time
Access and override the time at which a feature should be recorded.
Credit Bureau API
Integrate data from credit bureaus like Transunion.
Filters
Filter the rows of a
DataFrame by supplying conditions to the __getitem__() method.Tags
Tag related features.
Intermediate Feature Values
Cache intermediate feature values.
Preview deployments
Set up preview deployments for all PRs.
Account Takeover
Aggregate failed logins over a Kafka stream.
Override Cache Values
Supply a feature value in the input to skip the cache and any resolver entirely.
Creating DataFrames
Describe features at a feature class or feature level.
Descriptions
Describe features at a feature class or feature level.
Aggregations
Compute aggregates over a
DataFrame.Has Many
Define a has-many relationship between feature classes.
Has One + Has Many
Define a has-many relationship between feature classes.
Has One
Define a has-one relationship between feature classes.
Install Chalk CLI
Install the Chalk CLI in a GitHub Action.
Query Scalars
Query scalars with SQL files or strings.
Latest Computed Value
Cache the last computed example of the feature.
Stream SQL Aggregation
Compute an aggregation on windows using DataFrames.
Projections
Scope down the set of rows available in a
DataFrame.Feature Types
Create a namespaced set of features.
Owners
Assign owners to features for monitoring and alerting.
Identity Verification
Make use of vendor APIs to verify identities, control costs with Chalk's platform.
Tagged Resolvers
Trigger special behavior with tags.
Integration tests
Test interactions between resolvers with preview deployments.
Scalar Resolvers
Create a resolver that returns a single feature.
Multi-Feature Resolvers
Create a resolver that returns many features.
Primary Keys
Set the primary key for an entity.
Projections with Filters
Compose projections and filters to create a new
DataFrame.Device Data
Easily listen to streaming data and parse messages with custom logic.
Historical Data
Access historical sensor data as-of any time in the past.
Stream DataFrame
Compute a streaming window aggregate function using DataFrames.
Stream SQL
Compute a streaming window aggregate function using DataFrames.
Tags & Owners
Assigning tags & owners to features.
Override Max-Staleness
Set max-staleness per-request.
Sampling Cron
Pick exactly the examples that you’d like to run.
Returns
Identify transactions returned for non-sufficient funds.
Constructing Features
Create sets of features from your feature classes.
Basic Caching
Cache feature values rather than computing them realtime.
Aggregate Tradelines
Aggregate user statistics across tradelines.
Multiple Accounts
Identify users with multiple accounts.
Filtered Cron
Run resolvers on a schedule and filter down which examples to consider.
Cron
Run resolvers on a schedule with all possible arguments.
Multi-Tenancy
Serve many end-customers with differentiated behavior.
Query DataFrames
Query many rows and take advantage of push down filters.