The power and beauty of in-memory computing projects are that they truly do what they state -- deliver outstanding performance improvements by moving data closer to the CPU, using RAM as a storage and spreading the data sets out across a cluster of machines relying on horizontal scalability.
However, there is an unspoken side of the story. No matter how fast a platform is, we do not want to lose the data and encounter cluster restarts or other outages. To guarantee this we need to somehow make data persistent on the disk.
Most in-memory computing projects address the persistence dilemma by giving a way to sync data back to a relational database (RDBMS). That sounds reasonable and undoubtedly works pretty well in practice, but if we dig deeper, you’ll likely encounter the following limitations: