To talk about semi structured data, we first need to explain what data modeling is. Data modeling defines the logical structure of the database. Data modeling determines how data is stored, organized, and then manipulated in a database.
Understanding the different types of data your business stores is essential to developing an effective data management strategy. However, many people we meet don’t understand the differences between these types of data and why they need a different approach to data management.
In order to understand data, it must be effectively organized, stored and analyzed. For this reason, it’s important to know the nature of the data – especially whether it’s structured or not. Structured data is entered into predefined fields and can be arranged in tables or relational databases, whereas unstructured data is heterogeneous and is not linked to standard fields.
However, there is a third type of data that sits between structured and unstructured data and this is known as semi-structured data. Semi-structured data does not have a rigid schema and therefore does not fit into any data table or relational database structure.
However, there are defined classification features, i.e. tags or internal semantic metadata or tags that enable analysis. In this post we will look at what is semi-structured data.
Semi structured data is one of many types of data. In terms of data classification, it is one of three: structured data, unstructured data, and semi-structured data.
Structured data has a long history and is used extensively in organizational databases. Recently, however, semi-structured and unstructured data have come to the fore as technology has developed so that this data can be used and extracted for business insights.
Before that let us see Big Data in Semi structured data as it is one of the pillars of big data to understand better!
Table of Contents
Big Data
The concept of big data emerged because of the enormous increase in data volume. As a result, organizing and sorting data becomes difficult. The term “big data” usually describes mixed data sets that are very large and are a mixture of structured and unstructured data.
It is the process by which this big data is collected, evaluated and processed from multiple sources. Examples of big data are Facebook, the stock exchange, search engines like Google and data generated by airways.
Usually the nature of big data is described by 3Vs.
- Volume
- Velocity
- Variety
Volume is the key differentiator for classifying big data. All major social media sites regularly receive large amounts of data in terabytes / kilobytes. It becomes very difficult to control such data using traditional methods. Certain data is collected in files, records, and tables.
The second is velocity. This is the velocity of receiving and processing data. Typically about 2.5 trillion bytes of data are received each day. So it is impossible to trade using traditional methods.
The third is the variety. It refers to the unique sources from which data is collected. variety can change from structure to data category. Machine generated text, video, and images are a few different categories. Other popular features are reliability, value and variability.
Big data includes large volumes, high speeds, and an expandable variety of data. There are three types: Structured Data, Semi-Structured Data, and Unstructured Data.
Examples of Big Data
Here are some examples of “Big Data“:
- Facebook users send around 31.25 million messages and watch 2.77 million videos every minute !!
- Walmart customer transactions provide the company with approximately 2.5 petabytes of data per hour.
In this article let us see in detail about one of the types of big data that is Semi-Structured Data.
What is semi-structured data?
Semi structured data is information that cannot be found in relational databases or other data tables, but still has some organizational characteristics that make it easier to analyze, such as: semantic tags.
A good example of semi-structured data is HTML code that doesn’t limit the amount of information you want to collect in the document, but still enforces hierarchy via semantic elements.
Semi structured data in big data is data that does not fit into the data model, but has a certain structure. There are no fixed or rigid circuits. The data is not in a relational database, but has several organizational properties that facilitate analysis. With multiple processes we can store them in a relational database.
Where does semi-structured data fit in?
Semi-structured data is in the middle between structured and unstructured data. It contains certain aspects that are structured and other aspects that are not structured. For example, X-rays and other large images consist mostly of unstructured data in this case, lots of pixels.
It’s impossible to find and query these X-rays in the same way a large relational database can be searched, viewed, and analyzed. After all, you’re only looking for pixels in an image. Fortunately, there is a solution.
Although the file itself cannot contain more than pixels, words, or objects, most files contain small pieces called metadata. This opens up the possibility to analyze unstructured data.
Examples of semi-structured data are
- JSON (this is the structure DataAccess uses by default)
- XML
- CSV file
- usage, web and server logs
- Excel, CSV etc.
- NoSQL databases
- HTML
- Electronic data interchange (EDI)
- RDF
Example of semi structured data
Semi-structured data is not structured correctly in cells or columns. However, it contains elements that make it easy to separate fields and records. This can be a comma or colon or something else about the subject. An example of semi-structured data is a JSON query.
[{First name: “Howard”, last name: “Dean”, order number: “3584752”, total order: “12.34”}]
The data can be formatted into structured data by simply executing a few commands. If we analyze this analogy, we can see that structured data is less flexible, more organized, and stored in a certain format. Unstructured data is more complex and usually provides quality information that cannot be compared to predefined data models. In contrast, semi-structured data contains properties of both types.
Example of semi structured data examples in big data
Personal data stored in XML files:
Semi structured data characteristics:
- The data does not fit into any data model, but has a certain structure
- Data cannot be stored in rows and columns like in a database
- Semi-structured data contains tags and elements (metadata) which are used to group data and describe how the data is stored
- Such objects are grouped and arranged in a hierarchy
- Objects in the same group may or may not have the same attributes or properties
- There is not enough metadata, making it difficult to automate and manage data
- The same size and type of attributes within a group can vary
- Due to the lack of a well-defined structure, it cannot be easily used by computer programs.
Sources for semi-structured data
- XML and other markup languages
- Binary executable
- TCP / IP packets
- Compressed or zip files
- Integrate data from multiple sources
- Web page
Advantages of semi-structured data
- Data is not limited by any fixed schema
- Flexible, that is, the circuit can be changed easily
- The data is portable
- Structured data can be viewed as semi-structured data
- This supports users who cannot express their requirements in SQL
- It can easily handle heterogeneity of sources.
Extraction of information from semi-structured data
Semi-structured data have a different structure due to the heterogeneity of their sources. Sometimes they have no structure at all. This makes it difficult to tag and index. So extracting information from them is hard work. Here are possible solutions:
- Graphical models (eg OEMs) can be used to index semi-structured data
- With OEM data modeling techniques, data can be stored in a graphical model. Graphical data is easier to find and index
- XML allows data to be organized in a hierarchical order so that it can be indexed and searched
- Use of various data collection tools
How To use semi-structured data
The use of semi structured data allows us to integrate data from different sources or to exchange data between different systems. Applications and systems need to evolve over time. However, if we were working with purely structured data this would not be possible. Let’s take a look at the web form.
You may want to modify forms and collect different data for different users. If you are using a traditional relational database, the database schema must be changed whenever new fields are required and fields should not be left blank.
With semi structured data, you can collect any data in any structure without having to make changes to the schema or database coding. Adding or removing data does not affect functionality or dependability.
When you work with semi-structured data, you get a flexible presentation and don’t have to make configuration or code changes as the data evolves over time. Data from multiple sources with different notations and meanings can be collected and used.
The link is described as a reference and is entirely contained in a higher level object (tree). Semi structured data allows complex types of data structures and storage requirements to be managed and managed while maintaining the relationship between complex objects and schemas. Queries and reports can now be performed for many systems and data types.
Challenges in working with semi-structured data
While semi-structured data increases flexibility, the lack of a fixed schema also poses challenges for storage and indexing. Schema and data are closely related and interdependent, and a single query can update both. It is also a challenge to implement the requirements.
OEM and XML formats help store and share semi-structured data and can overcome some of these challenges.
As the volume of semi-structured data continues to grow, new ways to manage, compare, integrate, store and analyze will develop. Semi-structured data can help us capture and process data as-is without forcing it into unnatural structures.
Knowing the nature of semi-structured data and how to use it is very important given the ever-growing volume of this type of data.
Importance of semi-structured data in Big data
The various advantages of processing big data are explained as follows:
- Improve customer service.
- Better operational efficiency.
- Use of external knowledge during the decision making process.
- The first risk identification with services and products.
- Detection of deficiencies, errors and fraud in the organization.
- Better look at sales service.
- Reduce costs and savings.
- Increase income and flexibility.
Conclusion:
Business data that doesn’t follow a fixed pattern is semi structured data . Much of the data you find on the web can be called semi-structured. Everything revolves around “semi structured data“. If you have any questions or want to learn more about something else visit our website.
Using hybrid cloud storage such as the Actian Avalanche makes working with semi-structured data easier by naturally ingesting JSON data and storing it in a relational database.