Kafka Partitions vs Mongo Sharding which one is better throughput

I am using Mongo Sharding to register page views on my website. We have hashed shard key to evenly distribute data in multiple shards. Then our aggregation queries run over time range at interval to aggregate this data and provide trends on site. We came across Kafka for write distribution for heavy load and this kind of streaming. I compared both systems and both provide distribution on partitions in a topic with leader follower approach. Kafka does it using multiple partition on different brokers with partition replication and Mongo does it with multiple shards which have replica sets. As aggregation query will always be on time range than it will go to multiple shards/ partitions always. My question is how we can compare which will provide better through put and run time scalability in case of heavy load as i understand both use same mechanism adding new partitions in case of Kafka or adding new shards in case of Mongo. Please provide suggestions.