OLAP
Last reviewed: 2026-05-29
Purpose: Online Analytical Processing and data warehousing concepts, with practical focus on Apache Pinot as a real-time OLAP database.
Contents
Overview
This folder covers OLAP (Online Analytical Processing) fundamentals including dimensional modeling, star schemas, columnar storage, and real-time analytics architectures. The training uses Apache Pinot as the primary hands-on OLAP database.
OLAP Concepts
- Star Schema & Snowflake Schema — Fact tables vs dimension tables, designing for analytical queries
- Columnar Storage — How column-oriented databases differ from row-oriented (OLTP) databases for aggregation-heavy workloads
- Pre-aggregation & Rollups — Materialized views, pre-computed aggregations for faster queries
- MOLAP vs ROLAP vs HOLAP — Multidimensional, Relational, and Hybrid approaches
Real-World Application
The companion Pinot training provides hands-on experience with:
- Real-time OLAP — Streaming Wikipedia edits into Pinot via Kafka for sub-second analytical queries
- Pinot SQL — Aggregation functions (count, distinctcount, filter), time bucketing (DATETRUNC), epoch conversion
- Streaming Ingestion — Kafka-Pinot REALTIME tables with transform functions and JSON path extraction
- Dashboarding — Streamlit and Plotly Dash frontends querying Pinot for live metrics