Why do we use data partitioning? For example, when moving data into a data warehouse using Kafka, how does partitioning help?

Partitioning in Kafka divides a topic into multiple segments, each stored on a different broker. This enables parallelism: multiple producers can write to different partitions concurrently, and multiple consumers can read from different partitions in parallel. Partitioning also provides fault tolerance; if one broker fails, only the partitions on that broker are affected. When ingesting data into a data warehouse, partitioning allows the downstream system to process data in parallel, improving throughput and reducing latency. Additionally, partitioning by a key (e.g., user ID or timestamp) ensures related records stay together, which simplifies downstream aggregation and query performance. In a data warehouse, you can mirror Kafka partitions to table partitions, enabling efficient range scans and incremental loads.