{"id":80466,"date":"2018-01-18T10:04:58","date_gmt":"2018-01-18T18:04:58","guid":{"rendered":"https:\/\/www.backblaze.com\/blog\/?p=80466"},"modified":"2025-12-12T06:57:31","modified_gmt":"2025-12-12T14:57:31","slug":"what-is-cloud-computing","status":"publish","type":"post","link":"https:\/\/www.backblaze.com\/blog\/what-is-cloud-computing\/","title":{"rendered":"Cloud Babble: The Jargon of Cloud Storage"},"content":{"rendered":"

\"Cloud<\/a><\/p>\n

One of the things we in the technology business are good at is coming up with names, phrases, euphemisms, and acronyms for the stuff that we create. The Cloud Storage market<\/a> is no different, and we\u2019d like to help by illuminating some of the cloud storage related terms that you might come across. We know this is just a start, so please feel free to add in your favorites in the comments section below and we\u2019ll update this post accordingly.<\/p>\n

Clouds<\/h2>\n

The cloud is really just a collection of purpose built servers. In a public cloud the servers are shared between multiple unrelated tenants. In a private cloud, the servers are dedicated to a single tenant or sometimes a group of related tenants. A public cloud is off-site, while a private cloud can be on-site or off-site \u2013 or on-prem or off-prem, if you prefer.<\/p>\n

Both Sides Now: Hybrid Clouds<\/h3>\n

Speaking of on-prem and off-prem, there are Hybrid Clouds or Hybrid Data Clouds depending on what you need. Both are based on the idea that you extend your local resources (typically on-prem) to the cloud (typically off-prem) as needed. This extension is controlled by software that decides, based on rules you define, what needs to be done where.<\/p>\n

A Hybrid Data Cloud is specific to data. For example, you can set up a rule that says all accounting files that have not been touched in the last year are automatically moved off-prem to cloud<\/a> storage. The files are still available; they are just no longer stored on your local systems. The rules can be defined to fit an organization\u2019s workflow and data retention policies.<\/p>\n

A Hybrid Cloud is similar to a Hybrid Data Cloud except it also extends compute<\/a>. For example, at the end of the quarter, you can spin up order processing application instances off-prem as needed to add to your on-prem capacity. Of course, determining where the transactional data used and created by these applications resides can be an interesting systems design challenge.<\/p>\n

Clouds in my Coffee: Fog<\/h3>\n

Typically, public and private clouds live in large buildings called data centers. Full of servers, networking equipment, and clean air, data centers need lots of power, lots of networking bandwidth, and lots of space. This often limits where data centers are located. The further away you are from a data center, the longer it generally takes to get your data to and from there. This is known as latency. That\u2019s where \u201cFog\u201d comes in.<\/p>\n

Fog is often referred to as clouds close to the ground. Fog, in our cloud world, is basically having a \u201clittle\u201d data center near you. This can make data storage and even cloud based processing faster for everyone nearby. Data, and less so processing, can be transferred to\/from the Fog to the Cloud when time is less a factor. Data could also be aggregated in the Fog and sent to the Cloud. For example, your electric meter could report its minute-by-minute status to the Fog for diagnostic purposes. Then once a day the aggregated data could be send to the power company\u2019s Cloud for billing purposes.<\/p>\n

Another term used in place of Fog is Edge, as in computing at the Edge. In either case, a given cloud (data center) usually has multiple Edges (little data centers) connected to it. The connection between the Edge and the Cloud is sometimes known as the middle-mile. The network in the middle-mile can be less robust than that required to support a stand-alone data center. For example, the middle-mile can use 1 Gbps lines, versus a data center, which would require multiple 10 Gbps lines.<\/p>\n

Heavy Clouds No Rain: Data<\/h3>\n

We\u2019re all aware that we are creating, processing, and storing data faster than ever before. All of this data is stored in either a structured or more likely an unstructured way. Databases and data warehouses are structured ways to store data, but a vast amount of data is unstructured \u2013 meaning the schema and data access requirements are not known until the data is queried. A large pool of unstructured data in a flat architecture can be referred to as a Data Lake.<\/p>\n

A Data Lake is often created so we can perform some type of \u201cbig data\u201d analysis. In an over simplified example, let\u2019s extend the lake metaphor a bit and ask the question; \u201chow many fish are in our lake?\u201d To get an answer, we take a sufficient sample of our lake\u2019s water (data), count the number of fish we find, and extrapolate based on the size of the lake to get an answer within a given confidence interval.<\/p>\n

A Data Lake is usually found in the cloud, an excellent place to store large amounts of non-transactional data. Watch out as this can lead to our data having too much Data Gravity or being locked in the Hotel California. This could also create a Data Silo, thereby making a potential data Lift-and-Shift impossible. Let me explain:<\/p>\n