Distributed database
What happens when your data outgrows a single machine? This episode introduces **Distributed Databases**, the architectural foundation for today's global, large-scale applications. Building on our knowledge of relational and NoSQL systems, we'll explore why we would want to spread a database across multiple computers. You will learn the core strategies of replication and partitioning used to achieve massive scalability and high reliability. We'll also confront the greatest challenge in distributed systems—maintaining data consistency—by conceptually introducing the famous CAP Theorem.
Check your understanding
These are the same multiple-choice questions you will see in the Quiz section after you listen to the episode. Use them here to preview or review the answers.
What is the fundamental characteristic of a distributed database?
- It can only store data in a relational model.
- The data is physically spread across multiple computers connected by a network.
- It is always less reliable than a centralized database.
- It eliminates the need for database indexes.
- All data is stored on one extremely powerful computer.
What are the primary advantages of a distributed database architecture?
- It guarantees that all transactions are faster.
- Improved reliability and high availability.
- The ability to scale horizontally by adding more servers.
- Reduced latency for globally distributed users.
- It simplifies the process of data normalization.
What is the technique of creating copies of the same data on multiple nodes to improve reliability and performance called?
- Partitioning
- Sharding
- Normalization
- Replication
- Indexing
The CAP Theorem describes a fundamental trade-off in distributed systems. Due to the reality of network partitions, systems must often choose between which two of the following guarantees?
- Scalability and Durability
- Security and Performance
- Consistency and Availability
- Normalization and Indexing
- Atomicity and Isolation
What is the main purpose of partitioning, or sharding, in a distributed database?
- To ensure all copies of the data are always identical.
- To split a large database into smaller pieces to enable horizontal scalability.
- To make the database schema simpler.
- To improve the speed of every single write operation.
- To create backups of the entire database on a single node.
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