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Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

Elasticsearch ILM automates data management through defined lifecycle phases.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

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

L4 and L7 load balancers differ in how they route network traffic based on layers of the OSI model.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

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

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

Fallbacks allow systems to handle failures safely and maintain user experience.

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

Microservices can communicate using five key patterns based on their needs.

Alerting helps notify the right people about production issues quickly.

Load shedding helps systems reject excess requests to maintain performance during high demand.

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