In today’s data-driven landscape, we are presented with numerous alternatives like Elastic Queries, Data Sync, Geo-Replication, ReadScale, etc., for distributing data across multiple databases. However, in this approach, I’d like to explore a slightly different path: creating two separate databases containing data from the years 2021 and 2022, respectively, and querying them simultaneously to fetch results. This method introduces a unique perspective in data distribution — partitioning by database, which could potentially lead to more efficient resource utilization and enhanced performance for each database. While partitioning within a single database is a common practice, this idea ventures into partitioning across databases.
Click through to see what the code looks like for this.