Object Storage can power not just websites with static resources but also serve as the intermediary data between mass migrations. Today many storage appliances for backup and de-duplication transfers data between drives and their own storage tier or hybrid vendor store. This backup and recovery items may be quite large with the size in terabytes. When these data transfers occur from one disk to another, they are opportunities to move the data to the object storage.
It is relatively easy to use object storage as time series buckets so that as one gets filled, data can start filling another. With the help of cold, warm and hot labels, it is easy to maintain progression of data. This data can then serve all the search queries over times series just like events in a time series database.
Today most of the organizations using private datacenters like to store large files and archives in the data centers. These datastores become increasingly unmanageable or costly to manage. Object storage offers a convenient way to save the data with the added benefit of programmability via S3 API.
Large staging area for data transfers and migrations are seen not only in storage appliances but also workflows involving storage. File-shares are a great example for saving data and there is already easy conversion of file-shares and files to buckets and objects in object storage. Since file-shares have been traditionally used for data, these now becomes an easy candidate for object storage.
Another area of usage for Object Storage is the massive extract-transform-load operations such as in Data Warehouse tasks. This is certainly a large source of data and often used for analytical purposes where results of an analysis may be used subsequently. Consequently, data needs to be saved and the object storage can be used for these purposes.
It is relatively easy to use object storage as time series buckets so that as one gets filled, data can start filling another. With the help of cold, warm and hot labels, it is easy to maintain progression of data. This data can then serve all the search queries over times series just like events in a time series database.
Today most of the organizations using private datacenters like to store large files and archives in the data centers. These datastores become increasingly unmanageable or costly to manage. Object storage offers a convenient way to save the data with the added benefit of programmability via S3 API.
Large staging area for data transfers and migrations are seen not only in storage appliances but also workflows involving storage. File-shares are a great example for saving data and there is already easy conversion of file-shares and files to buckets and objects in object storage. Since file-shares have been traditionally used for data, these now becomes an easy candidate for object storage.
Another area of usage for Object Storage is the massive extract-transform-load operations such as in Data Warehouse tasks. This is certainly a large source of data and often used for analytical purposes where results of an analysis may be used subsequently. Consequently, data needs to be saved and the object storage can be used for these purposes.
More discussion included here: https://1drv.ms/w/s!Ashlm-Nw-wnWtyVeqoXu7U9zEKuT
This example could be modified to use objects and bucket to stash the index generated from the content. AWS .Net SDK provides a way to save data to object storage in cloud.
This example could be modified to use objects and bucket to stash the index generated from the content. AWS .Net SDK provides a way to save data to object storage in cloud.
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