The Modern Data Stack
3. Pipelines & Ingestion
Building ETL pipelines for machine learning.
Step 01
Data Source
Raw inputs from databases, APIs, and logs.
Step 02
Ingestion Engine
Moving raw data into our environment.
Step 03
The Transformation Bot
AI Cleaning, Normalizing, and Enriching.
Bot Processing...
Step 04
ML-Ready Storage
High-performance storage for training.
Compute Engine
Distributed Spark Cluster
Environment
Dockerized Kubernetes
Storage Tier
Parquet Columnar Files
Why this matters
In the real world, data is messy, incomplete, and buried in multiple locations. Data Engineering is the "plumbing" that builds reliable roads for your AI to travel on.
The Architecture
Every pixel of an AI model comes from original training data. If your engineering is flawed, your model will eventually collapse due to "Technical Debt" or "Data Drift".