.: .:
.:
.:
|
|
.: .:
.:
.:
.:
.:
.:
|
| |
|
|
|
 |
Data Processing Solutions
Data Processing revolves around the data flow within the organization and how it moves between the inventory (storage) points to the applications, processes and people that require it. Both in the front-end where data ideally is converted into Information as well as the back-end where data is being manipulated for administrative purposes like back-ups, archiving, etc. Typical solutions in the Data Processing area are:
- Data Migration solutions
-
This solution revolves around moving data from one type of storage, application or environment to another and is multidirectional in nature. It does not make presumptions on the nature, type or age of data. Just the fact that the data is moving (migrating) from one environment to another.
- Data Archiving solutions
-
This is a form of data migration, but it is one-directional. Data Archiving takes into account the age of the data and its diminishing importance and chance of becoming information over time. This implies that over time the need for immediate access decreases and the allowable recovery time increases. This allows for storing the data on cheaper and typically slower media. A Data Archiving solution also assumes that data is stored in a tiered environment. The process of moving data from Higher Tiered Storage to lower/cheaper Tiered storage is considered archiving.
- Data Reduction solutions
-
Refers to the process of cleaning data of duplicate, excess, redundant and un-allowed data and thereby reducing the amount of data and the resources it uses. Typical applications in this area use technologies like Single Instance storage and Data Deduplication. File and e-mail assessment tools for un-allowed types of data as defined by company policies (e.g. MP3’s) are also very popular methods to reduce the data set.
- Data Disposal solutions
-
Refers to the process of getting rid of data when it is no longer required, needed or desired to be around or, in other words, when data needs to disappear. Compliance regulation requires keeping data for a certain period of time, but keeping data longer than required can actually work against the interest of the organization.
- Data Virtualization solutions
-
Refers to emulating data sets, applications and sometimes whole environments. Typical solutions revolve around virtualizing servers where multiple server platforms are being reduced to one and the server functions are being taken over by so-called virtual servers and, to the client, it still looks as if multiple servers were available.
- Data Transition solutions
-
These solutions refer to the process of data being converted from one state of type to another. This also encompasses the more abstract function of data being converted into information, but this is something only the actual recipient determines and we don't go into detail on when the data is being considered information or not. Solutions in this category revolve around the nature of certain datasets and its specific requirements for processing like E-Mail, OLTP, Multi-Threading, etc.
Click here to contact Zibiz about our Data Processing solutions
|
|
 |
|
|
