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When it comes to data storage, the temperature must be taken into consideration. The level of layered data storage service, from cold to hot, is described by the temperature setting.
The levels are distinguished according to the level of significance for the business – in other words, how important data is for the business and how often it is accessed. Generally, the terms “cold” and “hot” mean where the data was located earlier (traditional file storage).
Accessed frequently, hot data is kept near the CPUs’ heat and the rotating drives. Cold data – data that is not required often – is kept on tape or a drive farther from the data center floor.
With the modern digital transformation era, traditional file storage systems are becoming obsolete and are being substituted by the latest software-based file systems. In addition, with the introduction of the cloud, things have changed dramatically, and virtual storage mediums are becoming more and more prevalent.
Let us find out the fundamental terms associated with data storage in terms of the current scenario and how to differentiate between them.
Hot storage is data that requires frequent access instantly. Any piece of information crucial for your business and needs to be retrieved regularly is deemed fit for hot storage.
The data is usually placed in a layered or hybrid storage system to enable fast data access for hot storage. Services catering to hot storage are more likely to do the following:
Use drives with the latest technologiesHave faster transport protocolsBe positioned either near to the client or in multiple regions
Owing to the resource-intensive storage requirements, cloud data storage providers charge a premium for hot data storage. Some popular service providers such as Amazon AWS and Microsoft’s Azure Hot Blobs offer services at hefty amounts.
Layering in Hot Storage
Data stored in the uppermost tier (high priority) should use solid-state drives. These drives are optimized to yield a high rate of transactions and lower latency than traditional hard drives. For other cases, hard disk drives are best suited for situations where access to the drive is heavy, as it showcases higher durability to intensive read and write cycles.
Regardless of the storage medium used, the jobs require instant and consistent response times in hot data storage.
Examples of tasks requiring such type of storage:
Interactive video editingCapturing telemetry data Web content MessagingOnline transactionsData transformation
Differentiating Cloud Services Based on Hot, Warm, and Cold
Distinguishing the storage type depends on the kind of storage architecture used:
For distributed systems using edge devices, hot storage can function as both computational memory and storage for each edge device.Pure cloud services function as cold computational memory and storage where any off-cloud device uses cold storage.
Identifying When to Use Hot Storage
Data required for hot storage includes:
Data that transforms at a faster paceData used for querying customer requestsData used in the latest real-time projects
Since hot storage requires instant and consistent access, cloud services like Google and Amazon have 99.95% accessibility, while Azure has up to 99.99%. Data coming from hot storage is known as “data streams.”
The speed of data transfer mainly depends on several routes from which the data passes through to reach from its host to its destination. Data processed nearest to its source will have a higher speed, while data traveling through different networks to reach the developer’s device will have a longer access time.
Cold storage is used for less frequently accessed data that does not require instant access like hot data. Such data consists of information that is no longer active and is not relevant. Some of the workable examples of data fit for cold storage include:
Outdated projectsFinancial data that needs to be recorded and maintained Data about legal and HR (human resources)Other requirements that need record keeping
The rate of retrieving data and response time for cold storage data systems is slower than services intended for managing active data. Good examples of cold cloud storage are Amazon Glacier and Google Coldline.
Cold data is best kept on storage mediums that provide lower speeds and are more affordable. Tape is one such cold data storage medium. LTO (Linear Tape-Open), developed in the late 1990s, is also another option. To retrieve Linear Tape-Open (LTO) data, the tapes must be physically accessed from storage racks and fixed over a tape reading machine. LTO ranks among the slowest methods of storing data (i.e., coldest medium).
Charges for storing data over cold cloud storage are comparatively less than for warm or hot storage, but a higher per-operation cost is associated with cold storage than other kinds of cloud storage.
What Comes with Cold Data Storage?
Cold data storage is purely offline storage, containing data that is not stored in the cloud. It is ideal for data that is stored on some tangible medium located in a secure environment having no access to the internet. Such data needs to be kept away from the world of the internet (e.g., cryptocurrencies like Bitcoin).
When to Use Cold Storage
Data meant for cold storage – such as legal causes, agreements, or records – stays for quite some time. Since data-versioning is becoming prevalent, old versions of datasets are best suited to be placed in cold storage. This data has not been updated recently but is being queried, also known as “dormant data.”
Retrieving cold storage data takes more time than hot storage. Accessing cold storage data can be done by physically sifting through a set of hard drives and connecting to a computer for retrieving the data.
When to Use Warm Storage
Data that requires continuous access without the restrictions forced by cold storage is fit for warm storage. Warm storage can be in the form of a network-enabled storage drive or a file server at a remote location for a business network.
If you are concerned about overloading the hot storage, files can be stored on warm storage. It will not free up space or resources but protect the data from being lost. Such alternatives are the best option for people in businesses that can keep:
Store guidesTutorials Data infrequently accessed, such as documents on a higher-capacity shared drive for employees
AI Is Redefining Data Storage
Data only grows bigger and bigger and, at present, has reached the Zettabyte Age. The future of technology is artificial learning (AI), Deep Learning (DL), or machine learning (ML), and data is life-blood.
However, when it comes to AI, DL, or ML, data storage can’t be defined as one-size-fits-all. Here, the concept of analytics comes into effect with varied storage requirements depending upon capacity, throughput, latency, IOPS, etc.
The infrastructure that brings out the full potential of AI and ML technology is data growth. And this is precisely why a massive amount of training data is needed to increase the accuracy levels of the predictive environment where the data need to be ingested, stored, and prepared.
However, artificial intelligence (AI) is redefining and revamping the concept of hot and cold data storage. As explained by Alper Ilkbahar, vice president and general manager of data center memory and storage solutions at Intel, “Simply storing images in the cloud is cold, while using AI to recognize faces in images is hot.”
Businesses of all sizes generate a massive volume of data every day. This calls for efficient Data Management strategies, especially storage and maintenance. But, first, you need to identify which solution suits your requirements, such as range of expenditure, data needs, and complexity.
Whether you go for hot or cold storage, the most crucial thing to consider is your data usage. If you want quick and easy access, a combination of local storage and a cloud provider will be the right choice for your data.
In the case of long-term storage, a mix of cold storage or a backup provider will be ideal. Such solutions offer reduced storage costs and free up local storage for other data.
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