File Name: structured semi structured and unstructured data .zip
When a conversation turns to analytics or big data, the terms structured, semi-structured and unstructured might get bandied about. These are classifications of data that are now important to understand with the rapid increase of semi-structured and unstructured data today as well as the development of tools that make managing and analyzing these classes of data possible.
Data that is the easiest to search and organize, because it is usually contained in rows and columns and its elements can be mapped into fixed pre-defined fields, is known as structured data.
Think about what data you might store in an Excel spreadsheet and you have an example of structured data. Structured data can follow a data model a database designer creates - think of sales records by region, by product or by customer. This makes structured data easy to store, analyze and search and until recently was the only data easily usable for businesses. Today, most estimate structured data accounts for less than 20 percent of all data. Structured data can be created by machines and humans.
Examples of structured data include financial data such as accounting transactions, address details, demographic information, star ratings by customers, machines logs, location data from smart phones and smart devices, etc.
A much bigger percentage of all the data is our world is unstructured data. Think of the text of an email message. The lack of structure made unstructured data more difficult to search, manage and analyse, which is why companies have widely discarded unstructured data, until the recent proliferation of artificial intelligence and machine learning algorithms made it easier to process.
Instead of spreadsheets or relational databases, unstructured data is usually stored in data lakes , NoSQL databases, applications and data warehouses.
The wealth of information in unstructured data is now accessible and can be automatically processed with artificial intelligence algorithms today.
This technology has elevated unstructured data to an extremely valuable resource for organizations. Beyond structured and unstructured data, there is a third category, which basically is a mix between both of them. Email messages are a good example. While the actual content is unstructured, it does contain structured data such as name and email address of sender and recipient, time sent, etc.
Another example is a digital photograph. The image itself is unstructured, but if the photo was taken on a smart phone, for example, it would be date and time stamped, geo tagged, and would have a device ID. A lot of what people would usually classify as unstructured data is indeed semi-structured, because it contains some classifying characteristics.
In a structured interview, the interviewer follows a strict script that was defined by the human resources department and is followed for every candidate. Another form of interview is an unstructured interview. In an unstructured interview, it is entirely up to the interviewer to determine the questions and the order they will be asked or even if they will be asked for every candidate.
A semi-structured interview takes elements from both structured and unstructured interview classifications. It uses the consistency and quantitative elements allowed with the structured interview but offers the freedom to customize based on the circumstances that are more in line with an unstructured interview.
So, for data, structured data is easily organizable and follows a rigid format; unstructured is complex and often qualitative information that is impossible to reduce to or organize in a relational database and semi-structured data has elements of both. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things.
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Unstructured Data Unstructured data encompasses everything that isn't structured or semi-structured data. Text documents and the different kinds of multimedia files audio, video, photo are all types of unstructured data file formats. The reason all of this matters is because a cloud data lake allows you to quickly throw structured, semi-structured, and unstructured datasets into it and to analyze them using the specific technologies that make sense for each particular workload or use case. Table compares the three data types. Table Qualities of structured, semi-structured, and unstructured data Structured data Semi-structured data Unstructured data Example RDMS tables, columnar stores XML, JSON, CSV Images, audio, binary, text, PDF les Uses Transactional or analytical stores Clickstream, logging Photos, songs, PDF les, binary storage formats Transaction management Mature transactions and concurrency Maturing transactions and concurrency No transaction management or concurrency Version management Versioned over tuples, rows, tables Not very common; possible over tuples and graphs Versioned as a whole Flexibility Rigorous schema Flexible, tolerant schema Flexible due to no schema Storage Management in the Cloud In Chapter 5, we look at how data life cycle management is a policy- based approach to managing the flow of a system's data throughout its life cycle—from creation and initial storage to the time when it becomes obsolete and is deleted.
Semi-structured data  is a form of structured data that does not obey the tabular structure of data models associated with relational databases or other forms of data tables , but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Therefore, it is also known as self-describing structure. In semi-structured data, the entities belonging to the same class may have different attributes even though they are grouped together, and the attributes' order is not important. Semi-structured data are increasingly occurring since the advent of the Internet where full-text documents and databases are not the only forms of data anymore, and different applications need a medium for exchanging information. In object-oriented databases , one often finds semi-structured data.
Flexibility and Scalability: Structured data is relational database or schema dependent therefore less flexible and difficult to scale, while semi-.
When we talk about data or analytics, the terms structure, unstructured, and semi-structured data often get discussed. These are the three forms of data that have now become relevant for all types of business applications.
When a conversation turns to analytics or big data, the terms structured, semi-structured and unstructured might get bandied about. These are classifications of data that are now important to understand with the rapid increase of semi-structured and unstructured data today as well as the development of tools that make managing and analyzing these classes of data possible. Data that is the easiest to search and organize, because it is usually contained in rows and columns and its elements can be mapped into fixed pre-defined fields, is known as structured data. Think about what data you might store in an Excel spreadsheet and you have an example of structured data. Structured data can follow a data model a database designer creates - think of sales records by region, by product or by customer. This makes structured data easy to store, analyze and search and until recently was the only data easily usable for businesses. Today, most estimate structured data accounts for less than 20 percent of all data.
Big Data includes huge volume, high velocity, and extensible variety of data. These are 3 types: Structured data, Semi-structured data, and Unstructured data. Writing code in comment? Please use ide. Skip to content. Related Articles.
In my previous blog post I talk about what data is.
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