What do you understand by Data? As we all know data is nothing but statistics, facts, or information about a particular entity. So, by this, we can understand how important data could be. Let us imagine a scenario over here. One day suddenly you lost all your contacts which are saved on your mobile phone. And these contacts are also a type of data that is stored in your mobile phone, which can be of an individual or a group of people. Now, if you lose all these contacts, just imagine how frustrating this could be?! Right?! And that is why we back it up with our Email IDs so that you can retrieve them at any point in time and anywhere in case of accidental deletion.
Now, talking in technical terms from an IT perspective, when you are running an IT infrastructure the most important entity is the data that is getting stored and the security of that data that is present and backed up by the services. Particularly, when that data is in the cloud, security is really a great concern.
If you are running this IT infrastructure on your Amazon Cloud, we know that EC2 has Elastic Block Storage (EBS) attached to it which stores all the EC2 data. And we know the best way to properly backup your data is to take snapshots of the EBS volumes in case there is an accidental deletion or hardware degradation at the cloud provider’s side. Do you think that taking snapshots alone can be enough in this case? NO! The other attributes like retaining these snapshots, automation, and deletion of the snapshots must also be taken into consideration.
In this blog, we are going to cover the use of Amazon Data Life Cycle Manager (DLM) policies to automate the retention, creation, and deletion of EBS snapshots.
Amazon Data Life Cycle Manager(DLM)
Most organizations are now migrating to Cloud, but there are scenarios like servers’ downtime being affected by several web attacks which can lead to data loss are not acceptable. So, Data lifecycle management should also be achieved in Cloud Computing, and in AWS (Amazon Web Services) specifically, this is resolved by a service called AWS Data Lifecycle Manager.
Data lifecycle management helps in setting up policies that will govern where the data is stored, for how long it can be stored (retention period), and who can have access to it, among other things. It not only increases efficiency but also has the showcases potential of a better data protection policy. Many organizations are easily able to comply with the many standards that are being imposed on businesses today.
Data lifecycle management is an approach to managing the data in several stages, from creation/capture of data to deletion of data. This helps organizations to protect their data from any unexpected deletion, loss, cyber-attacks, and more. From Data lifecycle management an organization will be able to monitor or track how its data is treated, processed, used, saved, and shared. Below you can refer to a simple stage of Data in a data life cycle.
Goals of Data Life Cycle Management
The three main goals of Data Lifecycle Management are:
Integrity – making sure that to all the users the data is consistent.
Confidentiality – Those with authorization can only be given access to the data.
Availability – when the data is required it can be accessed.
Advantages of Data Life Cycle Management
With the above-said goals, there are many advantages in the daily operations of a company that implements data lifecycle management and ensures the effectively handling information in hand.
Accurate and clean data is available for all the users, which in turn increases the efficiency and agility of the company’s process.
To assure the availability of data it is well maintained and extracted throughout the data cycle.
Data Lifecycle Management allows you to meet the requirements set forth by each individual industry sector for its data storage.
DLM also makes sure of the data’s safety during an event of risk or emergency.
With this, we have reached the end of our blog on data lifecycle management. I believe that now you can contemplate how an organization or company must handle its data in an accurate and effective manner by providing an additional robust level of implementation.
Thank you for reading.
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