Introduction
PipeCat is a framework for declarative data pipeline orchestration, purpose-built to provide high-level abstractions, composability, and operational transparency across diverse data workflows. PipeCat enables platform and data teams to define pipelines as code—describing tasks, dependencies, and resource bindings in a simple, maintainable format. It supports batch and streaming jobs alike, integrating tightly with compute, storage, and observability stacks to ensure smooth data operations from ingestion to transformation and delivery.
Key benefits of using PipeCat include:
Declarative Pipeline Definitions: Pipelines are defined in YAML or JSON using a high-level schema, enabling versioning, validation, and modular reuse across teams.
Composable and Reusable Tasks: Supports task abstraction and composition, making it easy to share logic (e.g., file ingestion, deduplication, model scoring) across different pipelines and projects.
Built-in Scheduling and Retry Logic: Provides first-class support for periodic schedules, backfills, retries, and SLA monitoring—ensuring data freshness and operational resilience.
Tight Observability Integration: Exposes detailed pipeline metadata, task logs, lineage, and metrics through integrations with monitoring tools like Prometheus, Grafana, and DataHub.
Platform-Native Connectivity: Seamlessly connects to Cake’s internal data lakes, warehouses, model endpoints, and feature stores—supporting ETL, reverse ETL, and model orchestration use cases.
PipeCat is used to orchestrate everything from product analytics pipelines and model feature materialization to experimentation tracking and customer-facing data APIs. It plays a central role in unifying the data platform—bridging the gap between infrastructure, analytics, and AI teams. By adopting PipeCat, you can ensure that its data pipelines are modular, observable, and production-grade by default—empowering teams to deliver data with speed, safety, and confidence.
Important Links