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

Downsampling and aggregation reduce data volume while preserving essential trends.

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

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

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

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

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

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.

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

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

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

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

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

REST API design focuses on creating clean, efficient, and scalable web services.

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

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

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

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

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

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

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

Transactions ensure database operations are reliable using ACID properties.