Cloud computing and unstructured data for unstructured data

1) Structured data, simply put, is a database. It is easier to understand when combined with typical scenarios, such as enterprise ERP and financial systems; Medical HIS database; **Administrative approval; other core databases, etc. What storage solutions are needed for these applications?

It basically includes high-speed storage application requirements, data backup requirements, data sharing requirements, and data disaster recovery requirements.

2) Unstructured database refers to a database whose field length is variable, and the records of each field can be composed of repeatable or non-repeatable subfields, which can not only process structured data (such as numbers, symbols and other information) but also be more suitable for processing unstructured data (full-text text, images, sounds, film and television, super ** and other information).

In the face of massive unstructured data storage, Shanyan Massive Object Storage MOS provides a complete solution, adopts decentralized and distributed technology architecture, supports tens of billions of files and exabyte-level capacity storage, has efficient data retrieval, intelligent labeling and analysis capabilities, easily copes with the storage challenges of the big data and cloud era, and provides intelligent decision-making for enterprise development.

Unstructured data refers to data with irregular or incomplete data structures, no predefined data model, and inconvenient to use database two-dimensional logical tables to represent data. Includes all formats of office documents, text, XML, HTML, various reports, images and audio information, etc.

Data in computer information systems are divided into structured data and unstructured data. Unstructured data is in a variety of formats, standards, and technically unstructured information is more difficult to standardize and understand than structured information.

Features of unstructured data:

Analyzing data doesn't require a highly specialized mathematician or data science team, and companies don't need to hire IT elites to do it. The real analysis takes place at the user decision-making stage, where the department manager who manages a particular product segment may be a marketer who is responsible for finding the best campaign plan or a general manager who is responsible for the needs of the customer base.

End-users have the power, the right, and the motivation to improve business practices, and visual text analytics tools can help them quickly identify the most relevant issues and take timely action, all without relying on data scientists.

Structured data, also known as row data, is data that is logically expressed and realized by a two-dimensional table structure, which strictly follows the data format and length specifications, and is mainly stored and managed through relational databases.

Unstructured data is data with irregular or incomplete data structure, no predefined data model for side grandchildren, and it is inconvenient to use database two-dimensional logical tables to represent data.

Unstructured data is available in a variety of formats, standards, and is technically more difficult to standardize and understand than structured information.

Semi-structured data has a certain structure, which is a data model suitable for database integration.

That is, it is appropriate to describe data contained in two or more databases that contain similar data of different schemas.

It is also a foundational model for tagging services for sharing information on the web.

The difference between structured and unstructured data is gradually becoming clearer. Aside from the obvious difference between storing in a relational database and storing a non-relational database, the biggest difference is the ease of analyzing structured data versus unstructured data. There are mature analytics tools for structured data, but analytics tools for mining unstructured data are in their infancy and development.

And there is much more unstructured data than structured data. Unstructured data accounts for more than 80% of enterprise data and is growing at a rate of 55% to 65% per year. Without the tools to analyze this massive amount of data, the enormous value of enterprise data will not be realized.

Structured and unstructured data are two different types of data that differ significantly in how they are organized and processed.

Structured data is data that is organized in a certain data model and format, with well-defined fields, fixed data types, and relationships. It can be stored and represented in a database, or in a standardized data format (e.g., JSON, XML, etc.).

Unstructured data refers to data that does not have a clear data model and organizational structure, and does not have fixed fields and formats. It can be data in the form of text, images, audio, social posts, emails, etc. Unstructured data often has a lot of free text, diverse content, and irregular data structures.

To sum up, structured data has a clear organizational structure and data model, which is suitable for high-quality data, and can be easily stored, inspected, stored, queried and analyzed; Unstructured data is more complex to deal with, with no clear organizational structure, rich content and diverse forms, requiring specific techniques and tools to extract useful information.

The role of structured data

1. Data management and storage: Structured data can be stored and managed in an organized way, which makes data search, update, deletion and backup operations more efficient and convenient. Through structured data, a database system can be established to manage and store data in a unified manner.

2. Data analysis and mining: Structured data can be flexibly queried and analyzed through database query languages (such as SQL). Through the analysis of structured data, it is possible to discover relationships, trends, and patterns between data, which can be used for data mining and business decision-making.

The analysis of structured data can help businesses and organizations make more informed decisions and improve business efficiency and competitiveness.

3. Data exchange and sharing: Structured data can be exchanged and shared through standardized data formats (such as XML, JSON, etc.). This makes it easier for different systems to exchange and integrate data, and promotes cooperation and information sharing between different organizations and systems.

4. Data consistency and reliability: Through structured data, data constraints and rules can be defined and enforced to ensure data consistency and reliability. For example, by setting data uniqueness and integrity constraints, you can prevent redundancy, duplication, and errors in your data.