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

Merges results from both layers to provide comprehensive answers to user queries. 2. Immutability and the Source of Truth

Manages the master dataset (an immutable, append-only set of raw data) and precomputes views. It ensures perfect accuracy but has high latency.

Processes real-time data streams to provide low-latency updates. It compensates for the batch layer's lag but may sacrifice some accuracy for speed.

Breaking data into smaller chunks so multiple nodes can work in parallel.

Traditional systems often scale "up" by adding more power to a single machine. Big data systems scale "out" by distributing data across a cluster of commodity hardware. This requires: