Cake for Ingestion & ETL

Simplify data prep with open-source building blocks

Before you can build or deploy AI models, you need clean, structured, and accessible data. Cake streamlines this process with open-source components and orchestration designed for modern AI workflows. Ingestion and ETL (Extract, Transform, Load) is the foundation of every ML and LLM workflow, but traditional pipelines are brittle, expensive to maintain, and often stitched together with fragile integrations.

Cake provides a modular, cloud-agnostic ETL system optimized for AI teams. Whether you’re ingesting tabular data, scraping external sources, or harmonizing structured and unstructured inputs, Cake’s open-source components and orchestration logic make it easy to go from raw data to training-ready inputs.

Unlike legacy ETL tools, Cake was designed with AI-scale workloads and modern security standards in mind. You get out-of-the-box support for key compliance needs, seamless integration with vector databases like pgvector, object stores like AWS S3, and integration with popular ML/AI tools like PyTorch and XGBoost—all in a cloud-agnostic infrastructure. 

Key benefits

• Accelerate delivery without custom plumbing: Use best-in-class ingestion and ETL components to move faster without reinventing the stack. Integration with tools across the entire AI/ML stack saves you from hours of manual setup.
  
  
• Stay modular and cloud agnostic: Avoid lock-in and rigid architectures with a modular system that works across clouds and workloads. Deploy your ETL pipelines on any infrastructure (AWS, GCP, Azure, on-prem, or hybrid) while connecting to your existing tools via Cake’s open-source integrations.
  
  
• Ensure compliance from day one: Build pipelines that meet enterprise security and audit requirements from day one. Comply with regulations like SOC2 and HIPAA with minimal effort.