The traditional relational database management system (RDBMS) has limitations that force compromises on data processing systems, such as size restrictions and the separation of transaction processing from analytics. One common compromise is sharding, where customer data sets are split across multiple database instances to accommodate varying sizes, but this can lead to performance issues and require application-defined sharding schemes. A better approach is using a scalable database that can handle both transactions and analytics on the same platform, such as SingleStore, which supports scale-out architecture, automatic sharding, and distributed query processing, eliminating the need for custom application software and reducing the risk of performance problems. SingleStore also delivers phenomenal transaction rates and crazy analytics performance through its in-memory rowstore structures, multi-version concurrency control, compilation of queries to machine code, highly-compressed disk-based columnstore, and vectorized query execution with SIMD instructions. Additionally, it supports strong data integrity, high availability, disaster recovery, transaction support, intra-cluster replication, cluster-to-cluster replication, and online upgrades, making it an attractive option for developers who want to focus on application development rather than database management.