In massive distributed systems, it is often impossible to have data be perfectly consistent across all global servers at the exact same microsecond (the CAP Theorem). Best practices involve designing for , where the system guarantees that, given enough time, all nodes will reflect the same data, allowing for high availability in the meantime. 5. Data Compression and Serialization
The Foundation of Modern Data Systems: Principles of Scalable Big Data Big Data: Principles and best practices of scal...
A core principle of scalable systems is treating raw data as . Instead of updating a record (which creates risks of data loss or corruption), new data is simply appended. If an error occurs, you can re-run your algorithms over the raw, unchanging "source of truth" to regenerate correct views. This makes the system inherently fault-tolerant. 3. Horizontal Scalability (Scaling Out) In massive distributed systems, it is often impossible
Storing copies of data across different nodes to ensure the system stays online even if a server fails. 4. Eventual Consistency Data Compression and Serialization The Foundation of Modern
Merges results from both layers to provide comprehensive answers to user queries. 2. Immutability and the Source of Truth