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Cake for Ingestion & ETL

Automated ingestion & transformation pipelines designed for modern AI workloads—composable, scalable, and compliant by default.

 

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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. 

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.

Example use cases

Common scenarios where teams use Cake’s Ingestion & ETL capabilities:

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Model training prep

Automate ingestion from cloud buckets, APIs, and tabular stores to create model-ready datasets.

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Data harmonization

Combine and normalize structured and unstructured data for unified downstream processing.

Reduced Risk

Compliance-driven logging

Capture and transform sensitive data with audit-ready lineage and masking tools.

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Real-time data streaming

Ingest high-velocity event data from Kafka, Kinesis, or Pub/Sub for real-time analytics and alerting.

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Batch processing at scale

Use orchestrated pipelines to process large volumes of historical data on a schedule—without pipeline sprawl.

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Cross-cloud data unification

Consolidate data from multiple cloud services and SaaS tools into a single lake or warehouse with consistent schema and lineage tracking.

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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."

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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."

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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"

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Felix Baldauf-Lenschen
CEO and Founder

Frequently asked questions

What is data ingestion and ETL?

How does Cake support ingestion and ETL workflows?

Can I use my existing ingestion and ETL tools with Cake?

What makes Cake different from traditional ETL platforms?

Does Cake help with real-time as well as batch ingestion?

Learn more about Cake

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