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Module 3

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Data Storage

Everything about persisting data: relational databases, NoSQL variants, sharding, replication, and choosing the right storage for your workload.

8 lessons~110 min

What you'll learn

  • •Explain ACID properties and when they matter
  • •Choose between SQL and NoSQL based on data access patterns
  • •Design a sharding strategy and handle rebalancing
  • •Set up replication for high availability and read scaling
  • •Understand specialized stores: time-series, graph, object storage
1

Relational Databases & ACID

ACID properties, normalization, indexing strategies (B-tree, hash), query optimization, and when relational databases are the right choice.

18m
High
2

NoSQL Database Types

Four NoSQL families: document stores (MongoDB), key-value (Redis), wide-column (Cassandra), and graph databases (Neo4j). When to use each.

15m
High
3

Database Sharding & Partitioning

Horizontal vs vertical partitioning, shard key selection, range vs hash partitioning, hot spots, and rebalancing strategies.

18m
High
4

Database Replication

Master-slave, master-master, and quorum-based replication. Replication lag, conflict resolution, and trade-offs between consistency and availability.

15m
High
5

SQL vs NoSQL Decision Framework

A practical decision framework for choosing between SQL and NoSQL: data model, query patterns, consistency needs, and scaling requirements.

12m
High
6

Time-Series Databases

Purpose-built databases for time-stamped data: InfluxDB, TimescaleDB. Write optimization, retention policies, and downsampling.

10m
Low
7

Data Lakes & Data Warehouses

Structured vs unstructured data storage: data warehouses (Redshift, BigQuery) vs data lakes (S3 + Spark). ETL, ELT, and the lakehouse pattern.

12m
Medium
8

Object Storage (S3 Pattern)

How object storage works: S3-compatible APIs, eventual consistency, versioning, lifecycle policies, and when to use it vs block/file storage.

10m
Medium
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