Data Lake

Why a Data Lake?

Every AI system starts with data. But enterprise data is messy — it lives in CSVs, JSON APIs, plain text files, Markdown documents, and databases. Before an AI can use any of it, you need a unified ingestion pipeline that normalizes everything into a common format.

Think of it like this: a sales rep's world is scattered across CRM records, call transcripts, product specs, support tickets, and competitor intel. The Data Lake is the first step in making all of that searchable by AI.

Key Concepts

Document Interface

The core abstraction. Every piece of data — whether it's a CRM row, a call transcript, or a product spec — becomes a Document with:

This is the universal contract that every downstream module depends on. Get this right and everything else composes cleanly.

Loaders

One loader per data source. Each loader knows how to:

The pattern is intentionally simple — a function that takes a file path and returns Document[]. No frameworks, no magic. You can read any loader top to bottom and understand it completely.

Validation & Deduplication

Before documents enter the pipeline:

These checks catch data quality issues early, before they poison your embeddings and search results downstream.

Architecture Pattern

CSV ──→ CRM Loader ────┐
JSON ─→ Product Loader ─┤
TXT ──→ Transcript Loader ┤──→ Validate ──→ Document[]
JSON ─→ Ticket Loader ──┤
MD ───→ Competitor Loader ┘

Each loader is independent. You can add a new data source by writing one new loader — nothing else changes. This is the Open/Closed Principle in practice.

What You'll Build

Glossary