Step 2: Databases & Data Management

Navigation: Main Index | ← Step 1: Fundamentals | Step 3: Consistency vs Availability →

Overview

This step covers database technologies, data storage strategies, and data management techniques essential for building scalable systems. Understanding these concepts is crucial for making informed decisions about data persistence, replication, and distribution.

Topics Covered

1. SQL vs NoSQL

  • Relational vs non-relational databases
  • When to use each approach
  • Trade-offs and decision frameworks
  • Real-world examples and best practices

2. Which is Better and Use Cases

  • Decision-making framework for database selection
  • Hybrid approaches and polyglot persistence
  • Industry examples and patterns
  • Migration strategies

3. In-Memory Databases

  • Redis, Memcached, and other in-memory solutions
  • Use cases and performance characteristics
  • Persistence strategies
  • Best practices and common pitfalls

4. Data Replication & Migration

  • Replication strategies (master-slave, master-master)
  • Data migration techniques
  • Consistency models
  • Disaster recovery and high availability

5. Data Partitioning

  • Horizontal and vertical partitioning
  • Partitioning strategies and algorithms
  • Benefits and challenges
  • Implementation patterns

6. Sharding

  • Sharding strategies and architectures
  • Shard key selection
  • Rebalancing and resharding
  • Common pitfalls and solutions

Quick Navigation

TopicFileKey Concepts
SQL vs NoSQLsql-vs-nosql.mdACID, BASE, schema, scalability, consistency
Which is Betterwhich-is-better-use-cases.mdDecision framework, polyglot persistence, hybrid
In-Memory Databasesin-memory-databases.mdRedis, Memcached, cache, persistence, TTL
Data Replicationdata-replication-migration.mdMaster-slave, master-master, sync, async, failover
Data Partitioningdata-partitioning.mdHorizontal, vertical, range, hash, directory
Shardingsharding.mdShard key, consistent hashing, rebalancing, hotspots

Learning Path

  1. Start Here: Understand SQL vs NoSQL fundamentals
  2. Decision Making: Learn when to use which database type
  3. Performance: Explore in-memory databases for speed
  4. Reliability: Study replication and migration strategies
  5. Scale: Master partitioning and sharding techniques

Key Takeaways

  • Database Selection: Know when to use SQL vs NoSQL based on requirements
  • Performance: Understand in-memory databases for low-latency needs
  • Reliability: Master replication strategies for high availability
  • Scalability: Learn partitioning and sharding for large-scale systems
  • Migration: Understand data migration techniques and best practices
  • Step 1: Scaling concepts relate to database sharding
  • Step 3: Consistency models build on database fundamentals
  • Step 4: Caching complements in-memory databases
  • Step 6: Consistent hashing essential for sharding
  • Step 8: Microservices often use polyglot persistence

Study Tips

  1. Compare & Contrast: Create comparison tables for SQL vs NoSQL
  2. Draw Diagrams: Visualize replication topologies and sharding architectures
  3. Think Trade-offs: Every database choice has pros and cons
  4. Use Examples: Relate to real systems (Amazon DynamoDB, MongoDB, PostgreSQL)

Previous Step: Step 1: Fundamentals ←

Next Step: Step 3: Consistency vs Availability →

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