What is Unstructured Data?
Primarily, unstructured data storage is all the data that doesn’t fall under the horizon of relational databases. The data here is not structured using predefined data models. Still, it upholds an internal structure that may be textual or non-textual, generated by humans or machines. It gets stored within non-relational databases such as NoSQL.
Unstructured data can be anything from social media posts, images, audio files, sensor data, text data, and many different data types. The term unstructured highlights the fact that large datasets aren’t in a defined structure layout.
At first glance, unstructured data is a possible storage headache, but it provides a valuable source of intelligence and insight.
This article will underline unstructured data storage and the cloud services and structures that exist to ensure it works effectively in the Cloud, including NAS and object storage.
How Does Unstructured Data Storage Work?
Unstructured data has seen tremendous exponential growth because of the ongoing technological advancements driving eCommerce, cloud migration of businesses, and social media activities.
Also, excessive growth means that data storage has to get redefined
Regarding data size and format, unstructured data comprises everything, including IoT, remote system monitoring, and data to video and Images. File sizes can range from a few bytes to many gigabytes plus.
Cloud Storage of Unstructured Data
The Cloud provides high-performance, scalable storage infrastructure services to customers. There is increasing demand for such flexible services. Hence providers and pioneers of cloud innovations offer their infrastructure on a subscription basis or as open-source software to reduce the overall financial burden on business organizations.
Unstructured data pretty much includes every kind of information. The file sizes range from a few bits and bytes to gigabytes or more. But, there is no uniform approach regarding data storage. The type of storage used to store collected data depends on the computing capacity and the preset thresholds for input and output, including everything from low-performance cloud instances to high-performing, distributed files.
Network Attached Storage
Before, Network-Attached Storage (NAS) was just associated with single file, siloed data storage. Nowadays,scale-out NAS can handle big data and high-capacity data storage.NAS scaling has elevated file storage access into realms of higher performance and capacity.
Scale-out NAS has a parallel file system that provides a namespace across multiple attached storage boxes to scale billions of file data. You can add computing capacity and processing power in some cases.
However, object storage has also grown over the years and leads to unstructured data storage. Object storage provides advantages like unique identification for stored data, high performance, scalability, and easy API access. Hence, many cloud providers go for object storage.
Object storage is the more recent development of unstructured data storage that keeps data in a flat format. You can access the data using unique identification models with metadata headers that enable search and analysis. The service grew in popularity after providing an effective solution to the shortfalls of scale-out NAS.
Object storage is arguably the native format of the Cloud, too. It is hugely scalable and accessible via application programming interfaces (APIs), which fits well with the DevOps way of doing things.
Object storage falls short of file locking, and it recently improved in terms of performance.
The big cloud service companies have their primary storage offerings built on object storage. They offer different service tiers also, to cater to many business cases. For instance, Amazon web services provide various courses of S3 storage with variations determined by accessibility, speed, and the reproducibility of the data.
Unstructured Data vs. Structured Data
Unstructured data is data not managed using a transactional system. These include data not stored in a relational database management system. Otherwise, structured data comprises records and transactions within a database environment, for instance, the rows in an SQL database.
Both structured and unstructured data storage tools allow users to access information. Just unstructured data comes in significantly larger quantities than structured data.
Examples of unstructured data include:
Machine learning, artificial intelligence (AI), and Rich media. Including Media and entertainment data, surveillance data, audio files, weather data, and document collection files.Invoices, emails, Internet of Things (IoT), and productivity applications. Sensor and ticker data, Data Analytics.
Before the establishment of object-based storage, the majority of unstructured data got stored using file-based systems.
The Challenges of Working with Unstructured Data
Before attempting to take on the challenges of unstructured data, you must consider the problems businesses face with traditional unstructured data storage management approaches. Including:
- Scale: It’s pretty standard for many businesses to come across unstructured datasets with massive scales of tens or hundreds of billions of data files. The files or objects can be a few bytes (simple production-line reading) to terabytes (a full-length 8K video).
- Collaboration: Increasing amounts of unstructured data deliver immense value as they get transferred and shared. For example, research facilities at different hospitals share substantial genomic sequences. Otherwise, traditional approaches limit the capacity to share massive sets of unstructured data across regions and businesses. It attracts costly replication and governance.
Cloud Benefits of Unstructured Data Storage
The three leading cloud providers offer core object storage services for data lake storage use. For instance, Microsoft provides a targeted service, Azure Data Lake, to handle unstructured data. The primary benefits here include the expandable capacity provided as a means of transferring using different gateways.
The downside, of course, is paying to access the services as per your usage.
Also, hyper-scale services offer NoSQL cloud databases. These can include Google Datastore, Amazon DynamoDB, and Azure Cosmos DB. Some third-party providers also offer NoSQL databases that you can deploy using your cloud services.
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