Very clear and concise blog! Loved reading it. I'd be very curious to see how Amazon DMS performs at scale. Scale includes either of these situations: a) Larger

Very clear and concise blog! Loved reading it. I'd be very curious to see how Amazon DMS performs at scale. Scale includes either of these situations: a) Larger dataset - 2+TB b) Higher throughputs - WAL Spikes (at least 2-3K TPS) c) Reliably and efficiently replicating TOAST Columns (ex: large JSONBs). d) Advance data-types - BLOB, CLOB, HSTORE, ARRAYs etc. In my previous role with the Azure Postgres team, we observed that Azure DMS worked pretty seamlessly in migrations that did not involve the above situations. Once either of the above came in, the replication experience was hairy. Interestingly, in my current gig at PeerDB, many use-cases we are seeing have either of the above situations. We have been doing quite a surgical optimizations to handle those situations and they keep coming! That is the beauty of Postgres, enabling a myriad of workloads, each of the unique in their own way! :)