Cloud storage in object storage (AWS S3, Azure Storage, Google Cloud Storage) is widely used and it has well defined standards and protocols.
Object Storage has the best cost structure of any data storage mode/method.
Object Storage allows separating Data Storage from Compute which eliminates inefficiencies when both share a single set of resources
Object Stores can store files in their native format (JSON, CSV, XML, WITSML, EBCDIC, PDF, PostScript, GIF, JPEG, PNG, SVG, MOV, MP3, MP4, DVI, ePUB, Etc.) and these files can be accessed by their own native tools as well as many other tools.
Structured Data in an Object Store – Opening a file with a million records and loading it into memory is faster than querying a RDBMS and loading the results from the query into memory.
Structured Data in an Object Store – Saving a million records as a file within an object store is faster than connecting (Native, OLE, ODBC, JDBC) to a RDBMS (Native, OLE, ODBC, JDBC) and inserting or updating them.
Storing data within an object store allows most reporting, business intelligence, data integration, ETL, development, and other data tools can read data directly. Many tools optimized for working with large volumes of data work much faster and more efficiently when accessing data directly within an Object Store rather than from the results of an RDBMS SQL query. These tools are typically classified within the machine learning, data science, artificial intelligence toolsets but they can also be data profiling or analytics tools built to use large data sets and aggregate their results.
