Cloud Tools: Creating Efficient Cloud Storage with FathomDB

The blog post focuses on FathomDB—the solution that automates low-level database administration (DBA) tasks for MySQL owners.

FathomDB pros, cons, and recommended use

FathomDB is a relational database-as-a-service provided under the utility/service model. FathomDB administers a relational database in the cloud and absorbs the responsibility of maintaining a database server. Below, there is an overview of FathomDB’s features, possible drawbacks, and its typical use.

Pros

  • FathomDB leverages MySQL’s scaling capabilities across multiple machines, and an automated backup can make it a reliable cloud storage. Since a standard MySQL is offered as an engine, you do not have to change your application at all, and there is no lock-in or a new API to learn.
  • There also should be mentioned the FathomDB’s risk reduction, high quality level, and time-to-market improvement when it comes to putting a database in the cloud with an ability to scale.
  • The database’s performance analysis tools can facilitate the high-level DBA tasks and make them easier.
  • FathomDB allows complex database operations to happen according to user-configurable maintenance schedules. Furthermore, a disk storage space is unlimited on all plans, and higher memory and CPU shares give you a better performing database.
  • Finally, the tool enables companies to store their database on Amazon or Rackspace servers.

Cons

However, there are possible challenges that a company may face when starting database implementation in the cloud. In particular, it is the changing of a database instance’s size, which may sometimes involve restarting MySQL and even moving to a new server.

Recommended use

  • FathomDB can help a web-based companies to automate MySQL setup, administration, backup, maintenance, and monitoring.
  • The solution can suit the needs of small-scale projects that want to mature quickly. FathomDB can be particularly useful for startups that experience a high rate of growth and even become a foundation for large-scale systems with vast schemas and persisted data.

 

Further reading


The post was written by Sergey Bushik and Katherine Vasilega.

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