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Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

API pagination helps manage large datasets by breaking them into smaller parts.

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

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

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

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

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

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

Kafka uses state stores to remember previous events in stateful stream processing.

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

Transactions ensure database operations are reliable using ACID properties.

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

Observability helps engineers understand system behavior to debug production issues effectively.

Consistency models determine what data reads in distributed systems can see.

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

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

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

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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