Any concept.
clear in 60 seconds.

CDC with Kafka allows real-time updates from databases to multiple services.

Stream Processing Architecture helps turn incoming events into real-time insights efficiently.

Data residency is crucial for compliance with laws about where data can be stored.

Database per Service ensures each microservice owns its data, reducing dependencies.

Batch processing collects data to process later, while stream processing handles data in real-time.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Elasticsearch Data Streams simplify time-series data management with one name for many indices.

FAANG engineers use high-level design principles to create scalable systems.

Late-arriving events can be dropped, updated, or routed based on system needs.

Database replication enhances data availability and read performance but has challenges like replication lag.

HLD provides an overview of system architecture, while LLD details implementation specifics.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Kafka uses retention to keep historical data and compaction to keep the latest state.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

Elasticsearch offers Data Streams for simplicity and Time-Based Indices for control in managing time-series data.

Container data can be lost unless stored in persistent volumes.

Fault domains help prevent multiple servers from failing together in an architecture.

Replicas keep your system available, while snapshots allow for data recovery.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Choosing the right distributed lock depends on your system's needs and existing tools.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Redis is a fast in-memory data store used for caching and low-latency applications.

Stateless servers are easier to scale because they don't store user session data locally.