In terms of performance for the indexing, there are two pieces to consider:
1. **Access to the data **in blob storage. There are different ways to accomplish this: use the [distributed file system wrapper](https://community.denodo.com/docs/html/document/denodoconnects/8.0/Denodo%20Distributed%20File%20System%20Custom%20Wrapper%20-%20User%20Manual), leverage a data lake engine like Databricks, or use a [Denodo Presto cluster in AKS](https://community.denodo.com/docs/html/document/denodoconnects/8.0/Denodo%20Presto%20Cluster%20on%20Kubernetes%20-%20User%20Manual). Performance will be better with a data lake engine like Databricks or Presto than with the wrapper
2. **Indexing process**. Denodo supports two options: an embedded indexer distriubuted with Denodo Scheduler, based on Luzene, and [Elastic Search](https://community.denodo.com/docs/html/browse/8.0/en/scheduler/administration/creating_and_scheduling_jobs/data_sources/elasticsearch_sources). For large volumes, Elastic will yiled better results, but you will need to provide that infrastructure, for example using https://azure.microsoft.com/en-us/overview/linux-on-azure/elastic/
Additionally, make sure to follow the best practices for indexing outlined in[ this article](https://community.denodo.com/docs/html/browse/8.0/en/scheduler/administration/creating_and_scheduling_jobs/configuring_new_jobs/vdpindexer_extraction_section#recommendations-for-the-indexing-processes), and make sure to use incremental indexing whenever possible to avoid full refreshes
Regarding the search process, Elastic will also scale out better with large data volumes, as it can be clusterized and take advantage of parallel processing.