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Data warehouses help businesses analyze large amounts of data for better decision-making.

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.

Time-series databases are designed to store and query time-stamped metrics efficiently.

The choice between SQL and NoSQL databases depends on your application's specific needs.

OLTP handles transactions while OLAP is for data analysis and reporting.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

Choosing between single and multi-region architecture affects system resilience and cost.

Choosing between a shared database and a database per service affects system design significantly.

Sharding splits databases into smaller parts to improve performance and scalability.

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

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

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.

Time-based partitioning organizes time-series data into smaller, efficient segments for better querying.

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

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

Active-Active Architecture uses multiple regions to serve traffic simultaneously for better resilience.

Retention policies help manage how long data is kept in databases.

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

Backpressure prevents system overload by managing message processing rates in Kafka.

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

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.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

Cache penetration occurs when requests for non-existent data overload the database.

Backpressure helps manage message flow in Kafka during traffic spikes.