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

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

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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

Watermarks help stream processing systems manage event time and late events.

Tumbling windows are non-overlapping time segments, while hopping windows can overlap.

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

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

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

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

Older offsets can overwrite newer ones in Kafka if not handled properly.

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.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Latency, throughput, and response time are key metrics in system performance.

Latency is the time for one request, while throughput is how many requests are processed.

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

Vector clocks help track the order and concurrency of events in distributed systems.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Fetch limits control data size from Kafka, while poll records limit processed records.

Read-after-write consistency ensures users see their latest updates immediately.

Backpressure helps manage message flow in Kafka during traffic spikes.