Mails can be considered as unstructured data. Their usage by exchange clients is easily supported by unstructured storage. Unstructured storage has several benefits of traditional online relational stores. Moreover, it provides a new paradigm to support new use cases that go beyond browsing and searching. It relaxes the restrictions on mail store sizes.  The number of times a network is traversed also matters in the overall cost for data. The best cost for data is when data is at rest rather than in transit. The number of times a network is traversed also matters in the overall cost for data. The best cost for data is when data is at rest rather than in transit and object storage can serve as the sink for all mails and inbox clients need not store anything locally. Web clients can provide even more features with text mining. 

File-systems have long been the destination to store artifacts on disk and while file-system has evolved to stretch over clusters and not just remote servers, it remains inadequate as a blob storage. Data writers have to self-organize and interpret their files while frequently relying on the metadata stored separate from the files.  Files also tend to become binaries with proprietary interpretations. Files can only be bundled in an archive and there is no object-oriented design over data. If the storage were to support organizational units in terms of objects without requiring hierarchical declarations and supporting is-a or has-a relationships, it tends to become more usable than files. The modular storage described in this document enhance the use of object storage and do not compete with the usages of elastic file stores.    

As compute, network and storage are overlapping to expand the possibilities in each frontier at cloud scale, message passing has become a ubiquitous functionality. While libraries like protocol buffers and solutions like RabbitMQ are becoming popular, Flows and their queues can be given native support in unstructured storage. We discussed a  layer over object storage. Here we expand the definition and storage of Flows.  

Traditional relational databases have long cherished an acceptance for storing data that requires interpretations. However, the chores associated with converting data to structured form and amenable to querying can be relaxed with native support for rich non-hierarchical data organization from storage layer and transformation to a different class of unstructured storage.   

 While public cloud object storage solutions offer cloud-based services such as Amazon’s Simple Notification Services and Azure Notification hubs, on-premise object storage has the ability to make the leap from standalone storage offering to a veritable solution integration packaging. Public-Cloud offer robust myriad features for their general-purpose cloud services while the flows and quality of service are specialized usages of the object storage.  We use annotators to enhance the role of object storage in vectorized execution. Annotators add metadata in data transfers. When we inject annotations in the data flow we can manage them better without requiring any changes to source or destination.  

The ability to take the onerous routines of using object storage as a storage layer from the layer above across different data sources enables a thinner upper layer and more convenience to the end user. The customizations in the upper layer are reduced and the value additions bubble up the stack.    

An Object Storage offers better features and cost management, as it continues to stand out against most competitors in the unstructured storage. The processors lower the costs of usage so that the total cost of ownership is also lowered making the object storage whole lot more profitable to the end users.