Breaking data into smaller chunks so multiple nodes can work in parallel.
Storing copies of data across different nodes to ensure the system stays online even if a server fails. 4. Eventual Consistency Big Data: Principles and best practices of scal...
Merges results from both layers to provide comprehensive answers to user queries. 2. Immutability and the Source of Truth Breaking data into smaller chunks so multiple nodes
Storing and moving massive datasets is expensive. Best practices dictate the use of efficient serialization formats like or Parquet . These formats use columnar storage and schema evolution, which significantly reduce disk space and speed up analytical queries by only reading the necessary columns. Conclusion Eventual Consistency Merges results from both layers to
Manages the master dataset (an immutable, append-only set of raw data) and precomputes views. It ensures perfect accuracy but has high latency.
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)