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Cloud Tools: Creating Efficient Cloud Storage with FathomDB

Sergey Bushik

Today’s posting will be dedicated to Cloud storage solutions and focus on FathomDB—a database-as-a-service that automates low-level DBA tasks for MySQL owners.

Description

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

FathomDB Pros

  • FathomDB leverages MySQL’s scaling capabilities across multiple machines and automated backup can make it a reliable Cloud storage. Since standard MySQL is offered as an engine, you do not have to change your application at all, and there is no lock-in or new API to learn.
  • There also should be mentioned FathomDB’s risk reduction, high quality level, and time-to-market improvement when it comes to putting a database in the cloud with the ability to scale.
  • FathomDB’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; further, that disk storage space is unlimited on all plans and higher memory and CPU shares give you a higher performing database.
  • Finally, FathomDB enables companies to store their database on Amazon or Rackspace servers.

FathomDB Cons

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

FathomDB Recommended Use

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