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What is database maintenance?

Welcome to the Nightingale HQ overview of database maintenance services. Here we aim to introduce people to what they need to know.

Definition of Database maintenance

From Science Direct:

Database maintenance plans are a method of ensuring that a database is optimised and performing well.

What is database maintenance?

Executive view

Data is important to achieving your strategic goals and database maintenance ensures that your data is stored securely, is accurate, and is readily available to appropriate parties.

Database maintenance helps businesses:

  • reduce downtime and data loss.
  • ensure accurate and reliable data is available for modeling and insights.

Business function leader view

Database maintenance helps teams to ensure that accurate data is reliably available, that their databases are not being overloaded and that their data is secure.

You may need this service if:

  • your team experience database downtime.
  • you have a large volume of data and do not have maintenance or security measures in place.
  • your team is using data to gain insights that drive decision-making.

KPIs you should consider measuring for this are:

  • reduced downtime
  • increased speed of data availability
  • improved error logging
  • costs saved by reducing unnecessary load
  • reduced costs associated with data loss and corruption

Technical view

When implementing data science and AI solutions in your products, the availability, security and accuracy of your data are essential. Implementing database maintenance will improve the performance of your projects and make development smoother and more efficient.

Database maintenance helps deliver:

  • improved error tracking
  • data and log file management
  • index fragmentation
  • statistics
  • corruption detection
  • backups

Get this service if you encounter:

  • slow performance and high storage costs due to inefficient data storage or data inaccuracies.
  • poor file management.
  • difficulties accessing data.
  • occurrences of data corruption or data loss.

Key criteria to consider are:

  • most appropriate tech stack
  • data security