Ingestion & ETL | Cake AI Solutions

Overview

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.

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.

In-house pipelines

Total control (until it breaks): Custom Airflow or Spark pipelines offer flexibility, but demand constant engineering upkeep.

Every connector, retry, and alert is hand-built
Pipeline logic is hard to reuse across teams or projects
Changes to schemas or sources cause brittle failures
Observability, compliance, and access control are all bolt-ons

Result:

Full control, but growing technical debt and slow response to new needs

The Cake approach

Composable ingestion built for AI workflows: Use open-source tools like Airbyte, Beam, and Prefect—pre-integrated and production-ready.

Pipelines deploy in hours with built-in retries, alerts, and observability
Easily extend for LLM training, vectorization, or transformation
Deploy in your own cloud with enterprise-grade access control
Shared orchestration and logging across all your data workflows

Result:

Fast, reliable, and future-proof ingestion with less engineering effort

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"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

Scott Stafford
Chief Enterprise Architect at Ping

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"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

Felix Baldauf-Lenschen
CEO and Founder

What is data ingestion and ETL?

Data ingestion is the process of collecting data from various sources and moving it into a central location for storage and analysis. ETL (Extract, Transform, Load) goes a step further, transforming that raw data into a structured, usable format before loading it into your target system.

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About Cake

Cake for Ingestion & ETL

Overview