Types of big data
There are mainly three types of big data:
Structured Data
Structured data, in general, refers to data that has a set length and format. Numbers, dates, groups of words, and the numbers known as strings (for example, a customer's name, address, and so on) are examples of structured data. Most experts agree that this type of data accounts for about 20% of all data available. Structured data is what you're used to dealing with. It is typically saved in a database. You can query it using a language such as structured query language (SQL).
These data elements are frequently integrated into a data warehouse for analysis.
Unstructured Data
The information that does not have a predefined format is unstructured data. Unstructured data accounts for the remaining 80% of all data. However, until recently, the technology didn't support doing much with it other than manually storing or analyzing it.
Unstructured data is the most significant component of the data equation, and the use cases for unstructured data are rapidly expanding. Text analytics can analyze the unstructured text, extract critical data, and transform that data into structured information used in various ways.
For example, social media analytics with high-volume customer conversations is a famous big data use case. Unstructured data from call center notes, e-mails, written comments in a survey, and other documents are also analyzed to understand customer behavior better. To understand the customer experience, this can be combined with social media data from tens of millions of sources.
Semi-Structured Data
Semi-structured data is data that falls somewhere between structured and unstructured data. Semi-structured data does not have to conform to a fixed schema (structure), but it can be self-descriptive and have simple label/value pairs. Label/value pairs could include =Jones, =Jane, and =Sarah, for example. Semi-structured data examples include EDI, SWIFT, and XML. You can think of them as payloads for handling complex events.
Role Relational Databases in Storing Big Data
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When a database is modified, data persistence refers to how it retains versions of itself. The relational database management system is the great grandfather of persistent data stores (RDBMS). The computing industry used primitive techniques for data persistence in its early days.
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"Flat files" or "network" data stores were popular before 1980. These functional mechanisms were challenging to master and always required system programmers to write custom programs to manipulate the data.
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Edgar Codd, an IBM scientist, invented the relational model in the 1970s, and it was used by IBM, Oracle, Microsoft, and others. It is still widely used and has played a significant role in the evolution of big data.
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Although relational databases have ruled for decades, they can be challenging when dealing with large streams of disparate data types. On the other hand, relational database vendors are not standing still and are beginning to introduce relational databases designed for big data. In addition, new database models have emerged to assist people in managing large amounts of data.
- PostgresSQL (www.postgressql.org) is the most popular open-source relational database. Its extensibility and availability on a wide range of mainframes make it a foundation technology for some relational big data databases.
Role of CMS in big data management
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Databases are used by businesses to store unstructured data. They do, however, use enterprise content management systems (CMSs) that can manage the entire life cycle of content. Web content, document content, and other forms of media are examples of this.
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Enterprise Content Management (ECM) is defined as the "strategies, methods, and tools used to manage, capture, preserve, store and deliver content and documents related to organizational processes," according to the Association for Information and Image Management. This nonprofit organization provides education, research, and best practices.
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Document management, records management, imaging, workflow management, web content management, and collaboration are among the technologies included in ECM.
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A whole industry focuses on content management, and many content management vendors are expanding their solutions to handle large amounts of unstructured data. However, new technologies emerge to support unstructured data and unstructured data analysis. Some of these systems can handle both structured and unstructured data. Some allow for real-time streams. These include Hadoop, MapReduce, and streaming technologies.
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Content management systems designed to store content are no longer stand-alone solutions. They are more likely to be a larger data management solution component.
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For example, your company may monitor Twitter feeds, which can then be used to trigger a CMS search programmatically. Now, the person who initiated the tweet (perhaps looking for a solution to a problem) receives an answer that provides a location where the individual can find the product they are looking for. The most significant advantage is when this type of interaction is possible in real-time. It also demonstrates the value of leveraging real-time unstructured, structured (customer data about the person who tweeted), and semi-structured data.
- In reality, you will most likely use a hybrid approach to solve your big data problems. It makes no sense, for example, to move all of your news content.
Frequently Asked Questions
What are the key functions of CMS?
A typical CMS aims to assist users in efficiently managing information. The following are the primary functions of most CMS applications:
- Storing
- Indexing
- retrieval and search
- format administration
- revision supervision
- control of access
- Publishing
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Reporting
What are the different types of the content management system?
Five popular content management systems can assist you in organizing digital content:
- Component Content Management System (CCMS)
- Document Management System (DMS)
- Enterprise Content Management System (ECM)
- Web Content Management System (WCMS)
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Digital Asset Management System (DAM)
How does 'big data' help solve the content management system (CMS) problem?
Big data solutions such as MapReduce and Hadoop effectively resolve CMS content management issues.
What is the distinction between CMS and framework?
The primary distinction between a CMS and a framework is that a CMS is an application that creates and manages digital content. In contrast, a framework is a software that contains generic functionality that can be customized by additional user-written code depending on the application.
What are the various essential elements of a content management system?
The main components of a CMS are -
The data repository
user interface
workflow scheme
editorial tools
output utilities
Conclusion
In this article, we have discussed concepts of the role of CMS in big Data management organizations. We started with an introduction to the role of CMS, explored the concept of structured, unstructured, and semi-structured data, understood the concept of relational databases' role in Big Data, and then concluded with understanding the role of content management systems in big data management organizations.
We hope that this blog has helped you enhance your knowledge about the Role of CMS in big Data management Organizations and if you would like to learn more, check out our article Content Management System.
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