# 04 – Big Data & Analytics

When data volume, velocity, or variety exceed a single node, we enter the **big data** world.

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## 📌 Overview

- [ ] Understand the difference between **OLTP vs OLAP** systems
- [ ] Learn the building blocks of large-scale storage and analytics
- [ ] See how data flows from source → storage → processing → consumption

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## 🧱 Core Components

### [[Distributed Storage]]

- [ ] Learn why we need distributed file/object stores (HDFS, S3, GCS)
- [ ] Understand **replication**, **erasure coding**, and data locality

### [[Data Warehouses]]

- [ ] Understand columnar storage and MPP architectures
- [ ] Compare on-prem vs cloud warehouses (Snowflake, BigQuery, Redshift)

### [[Query Engines]]

- [ ] Learn about engines that sit **on top of storage** (Presto/Trino, Hive, Spark SQL)
- [ ] Understand separation of **storage and compute**

### [[Streaming Systems]]

- [ ] Understand **event streams** and **log-based architectures** (Kafka, Pulsar)
- [ ] Learn about streaming processing engines (Flink, Spark Streaming)

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## 🔗 Connections

- Align with [[01 - Fundamentals]] for replication, sharding, and consistency
- Connect with [[07 - Data Modeling]] for warehouse schemas (star/snowflake)
- Relate to [[05 - Cloud Databases]] for managed big data services