Yoda Response: Certainly, Avi. In one of my projects at Onclusive, we had to optimize a NoSQL database, specifically Elasticsearch, for high-velocity data. The data was being ingested from multiple sources in real-time, and we needed to ensure that our search and analytics queries were performant.

Here are the strategies we employed:

1. **Index Optimization**: We optimized our Elasticsearch indices by carefully designing the index mappings. This included defining appropriate data types for fields, using nested and parent-child relationships where necessary, and setting up index templates to ensure consistency.

2. **Sharding and Replication**: We configured the number of shards and replicas to balance the load and ensure fault tolerance. By distributing the data across multiple shards, we were able to handle higher write and read throughput.

3. **Bulk API**: To improve the performance of data ingestion, we used the Bulk API for batch processing of documents. This reduced the overhead of individual document indexing and significantly increased the ingestion rate.

4. **Query Optimization**: We optimized our search queries by using filters instead of queries where possible, as filters are cached and faster. We also used aggregations judiciously to avoid heavy computations that could slow down the system.

5. **Monitoring and Tuning**: We continuously monitored the performance of our Elasticsearch cluster using tools like Kibana and Elasticsearch's built-in monitoring features. This helped us identify bottlenecks and tune the cluster settings, such as thread pools and cache sizes, for optimal performance.

6. **Data Retention Policies**: We implemented data retention policies to manage the lifecycle of our indices. By periodically deleting old indices that were no longer needed, we kept the cluster size manageable and the performance high.

7. **Hardware and Infrastructure**: We ensured that our Elasticsearch nodes were running on high-performance hardware with sufficient CPU, memory, and SSD storage. We also used dedicated master and data nodes to distribute the workload effectively.

By employing these strategies, we were able to optimize our Elasticsearch database to handle high-velocity data efficiently, ensuring that our search and analytics queries remained fast and responsive.