Data & Infrastructure

Data Lake

A data lake is a central storage system that ingests structured, semi-structured, and unstructured data in its raw format — without prior schema adaptation. It serves as a flexible collection point for all enterprise data, enabling downstream analytics, AI training, and exploratory data analysis without rigid structure requirements.

Why does this matter?

For AI projects, a data lake is often the first step: before models can be trained or RAG systems built, all relevant data must be consolidated in one place. A data lake stores everything — from machine logs to customer correspondence to product images — making it accessible for future AI applications.

How IJONIS uses this

We implement data lakes on AWS S3, Azure Data Lake Storage, or MinIO (on-premise) with Delta Lake or Apache Iceberg as the table format. Data is organized in zones (Raw, Curated, Enriched), and automatic cataloging ensures your data remains discoverable even as volume grows.

Frequently Asked Questions

Will my data lake quickly become a data swamp?

The risk exists if no clear governance is established. We implement zone architecture, metadata cataloging, and automatic data quality checks from the start. This keeps your data lake organized and usable — even with terabytes of data.

Do I need a data lake or does a data warehouse suffice?

For purely structured data and fixed reports, a data warehouse suffices. Once you want to use unstructured data (PDFs, emails, images) for AI, you need a data lake. Modern lakehouse architectures combine both advantages in one system.

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