Why is SQLite faster than Redis in this simple benchmark?

The current answers provide insight as to why Redis loses this particular benchmark, i.e. network overhead generated by every command executed against the server, however no attempt has been made to refactor the benchmark code to accelerate Redis performance.

The problem with your code lies here:

for key in data:
    r.set(key, data[key])

You incur 100,000 round-trips to the Redis server, resulting in great I/O overhead.

This is totally unnecessary as Redis provides "batch" like functionality for certain commands, so for SET there is MSET, so you can refactor the above to:

r.mset(data)

From 100,000 server trips down to 1. You simply pass the Python dictionary as a single argument and Redis will atomically apply the update on the server.

This will make all the difference in your particular benchmark, you should see Redis perform at least on par with SQLite.

from the redis documentation

Redis is a server: all commands involve network or IPC roundtrips. It is meaningless to compare it to embedded data stores such as SQLite, Berkeley DB, Tokyo/Kyoto Cabinet, etc ... because the cost of most operations is precisely dominated by network/protocol management.

Which does make sense though it's an acknowledgement of speed issues in certain cases. Redis might perform a lot better than sqlite under multiples of parallel access for instance.

The right tool for the right job, sometimes it'll be redis other times sqlite other times something totally different. If this speed test is a proper showing of what your app will realistically do then sqlite will serve you better and it's good that you did this benchmark.