If this describes the majority of your problem space then Datomic is a good fit. Otherwise I would stick to a popular RDBMS like Postgres to leverage the community behind it. You will have a much easier time interacting with a well understood open source engine.
Preserving the history of all changes isn't just for reconstructing that history later, it's a central component in maintaining ACID while allowing for distributed readers.
Datomic has a singular transactor - node that can accept writes. So all transactions happen in sequence and consistency is maintained within the data model.
So your database goes
DB (0 transactions) -> DB' (1 transaction committed) -> DB'' (2 transactions committed)
With normal databases, when DB'' is created, DB' is lost. You change stuff in place. This means that anyone wanting to observe a consistent view of the database needs to coordinate with writes. This is why SQL dbs are single giant machines.
For Cassandra, Dynamo, Mongo, etc you still change stuff in place, but you just accept that things will be potentially inconsistent ("eventually consistent") for your readers. Once you drop that part of ACID you can scale your readers and your writers arbitrarily.
With Datomic DB, DB', and DB'' are still maintained. If you want to read the database "now" you can get a handle to the state that your reader thinks is the most recent. This might not be behind the absolute newest state that the transactor knows about, but it is still a consistent view of the database.
Its because of this you can scale your read nodes independently of the system. Because if you are reading DB' you know the value of that won't change you can load chunks of the indexes into the memory for caching and even embed the querying into the application making queries.
The proprietary-ness and JVM-ness of Datomic are its biggest weaknesses. I choose to believe those weaknesses can be addressed.
The simplicity of Datomic is very appealing, it's just a timestamped series of EAV entries, and some fancy indexing. Avoiding the latency (the database over there issue) sounds like a big win. Most of the simplicity is made possible by only having one single synchronous writer which is a major drawback you have to be able to live with. Some write-heavy applications would struggle.
Related to the timetravel feature I've bookmarked a blog post by Valentin Waeselynck where he talks about some of the misconceptions and drawbacks. [2]. My company have a not super-great setup with triggers on MySQL, and have longed for MariaDBs SQL 2011 compliant Temporal Tables which looks very interesting [3]. I guess an advantage with Datomic is that you don't have to create an entire table history row when all you want is to update a single field.
I was interested enough in Datomic that I started building a Python bridge but never finished it [4]. An added bonus was that I learned about the Transit serialization format which was ALSO super inspiring [5].
[1] https://www.youtube.com/watch?v=D6nYfttnVco
[2] https://vvvvalvalval.github.io/posts/2017-07-08-Datomic-this-is-not-the-history-youre-looking-for.html
[3] https://mariadb.com/resources/blog/temporal-tables-part-1/
[4] https://github.com/runekaagaard/brygge/blob/master/src/jolle/example.py
[5] https://cognitect.com/blog/2014/7/22/transitYou won't be able to "source" and recreate the past without some work and unexpected things will happen when you "source" the past with the most recent code.
It useful but the history aspect is easily misunderstood.
This can be done in different ways but with Datomic you can request a point in time database. This database doesn't exist so it needs to be "sourced" from history. This means running through everything that happened up to some point to create a snapshot of the database state (or query).