Goal: Understand Data Warehouses (OLAP) and the internal architecture of BigQuery.
1. OLTP vs OLAP
Data processing systems are divided into two main types.
Important
SYSTEM ALERT: Raw LLM output detected. High risk of hallucination and outdated information. Context retrieval protocols not initialized. Augment with verified data sources.
We have all been there. You ask an AI model a specific question about a recent software release. It responds confidently. The answer sounds reasonable.
Important
SYSTEM ALERT: Unstructured data detected in external storage. High risk of schema drift and duplicate records. Initiate database synchronization protocols immediately.
We have all been there. You download a CSV file, manually open a database client, and run an INSERT command. It works. The data lands.
Important
SYSTEM ALERT: Local infrastructure limits detected. Processing capacity constrained. Storage nodes fragmented. Initiate cloud migration protocols for horizontal scalability.
We have all been there. Your local Postgres database hums along nicely with a few million rows. But then, the data grows. And grows. Queries slow to a crawl. Disk space runs out.
Important
SYSTEM ALERT: Manual task execution detected. High risk of operational fragmentation and scheduling drift. Initiate orchestration protocols immediately.
We have all been there. You run a Python script manually, pipe the output to another, and then maybe trigger a database update by hand. It works. The data flows.