What are advanced Cassandra data modeling patterns?

Advanced Cassandra data modeling patterns: 1. Bucket pattern: limit partition size by bucketing: PRIMARY KEY ((user_id, week), event_time). Each week is a separate partition, preventing unbounded growth. 2. Queue / inbox pattern: simulate a queue with Cassandra: PRIMARY KEY (queue_name, created_at). Consumers delete messages after processing (using TTL or explicit delete). Cassandra is not ideal for queues due to tombstones — use sparingly. 3. Token-based pagination: WHERE token(partition_key) > token(?) for efficient cluster-wide pagination without ALLOW FILTERING. 4. Reference data with frozen collections: embed related data as FROZEN<LIST<address>> — stored as a blob, entire collection replaces on update. Good for rarely-changed nested data. 5. Table-per-query: create a dedicated table for each access pattern with the query's filter columns as the partition key. 6. Hybrid Cassandra + search: Cassandra for writes and primary storage, Elasticsearch for complex queries (full-text, multi-field filters). Sync via Kafka/Debezium. This pattern is used by many large-scale applications (Twitter, Netflix) to leverage Cassandra's write scalability with search's query flexibility.