Causal Cluster Performance Impact

I have a causal cluster and a HA instance of neo4j where I am trying to load huge datasets (probably 1 TB of data) and trying to identify or compare the metrics getting for loading data and for some HOP queries.
HA Instance: 1 AWS r4.2xlarge instance
Causal CLuster configs: 3 core servers (3 x r4.2xlarge)

While loading the data, the cluster taking too much time as I could see the same data is replicated to 3 instances in the causal cluster's case. Is there any way to reduce the replication and avoid this performance impact in the distributed mode?

My use case is to build a causal cluster for neo4j in such a way that it should have performance capability while comparing to single HA neo4j instance.

Note: I am using Neo4j Enterprise Edition 3.5.14

If you want to optimize for absolute highest throughput writes, you should do your mega load into a single instance, and then use the resulting database to seed a cluster.

Fundamentally -- in a cluster, before your write can be acknowledged, a majority of cluster members have to agree on the write. This implies network round-tripping between nodes, and slower overall write performance, but this is the same thing that also guarantees consistency and safety of your data.

There are various causal_clustering.* configuration parameters that can be tuned, and in your particular instance, you can probably get improved overall writes with causal cluster, but because of the fundamental technical approach, single instance loads will be fastest.