Kaskada

About Kaskada

Aggregate events to produce a continuous timeline whose value can be observed at arbitrary points in time

Every expression is associated with an “entity”, allowing tables and expressions to be automatically joined Entities eliminate redundant boilerplate code

Collect events as you move through time, and aggregate them with respect to other events Ordered aggregation makes it easy to describe temporal interactions

Pipe syntax allows multiple operations to be chained together Write your operations in the same order you think about them It's timelines all the way down, making it easy to aggregate the results of aggregations

Pivot from events to time-series Unlike grouped aggregates, generators produce rows even when there's no input, allowing you to react when something doesn't happen

Observe the value of aggregations at arbitrary points in time Timelines are either “discrete” (instantaneous values or events) or “continuous” (values produced by a stateful aggregations) Continuous timelines let you combine aggregates computed from different event sources

Shift values forward (but not backward) in time, allowing you to combine different temporal contexts without the risk of temporal leakage Shifted values make it easy to compare a value “now” to a value from the past

It is functions all the way down No global state, no dependencies to manage, and no spooky action at a distance Quickly understand what a query is doing, and painlessly refactor to make it DRY

Built on the latest in efficient, GC-free, columnar computation and packaged up to easily install and run locally on your existing hardware High-efficiency compute means most workloads fit on a single instance, but Kaskada is cloud-native so you can scale when needed

Declaratively express queries over partitioned, ordered streams without lossy mappings from streams to relational models Queries freely combine rich analytic transformations and aggregations with order-dependent temporal and sequential operations

UNIFIED BATCH AND STREAMING

Columnar compute allows you to execute analytic queries over large historical event datasets in seconds End-to-end incremental execution allows you to maintain real-time query results computed over event streams efficiently Kaskada's streaming-native query language means any query can be used, unchanged, for both purposes

What can you use Kaskada for?

Compute event-based features at arbitrary, data-dependent, points in time in historical feature computation Prevent data leakage, or accidental computation of future events that contaminate ML models

Compute events in batch or real time for marketing, sales, or business analytics applications

Analyze logs and events across multiple real time and batch sources for monitoring, troubleshooting, and threat detection Visualize aggregations over the full history of your events

Manage supply chain events and processes across locations providing a better end user experience across channels Dynamically adapt to changing resource availability in real-time

Provide a differentiated user experience with applications that respond dynamically to user actions and behavior Easily write sophisticated trigger conditions to implement real-time business logic