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

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

Data Lakehouse architecture merges flexible data storage with reliable analytics capabilities.

Downsampling and aggregation reduce data volume while preserving essential trends.

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

CDC with Kafka allows real-time updates from databases to multiple services.

Change Data Capture allows real-time syncing of database changes to other systems.

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