Processing Structured and Unstructured Data in a NoSQL Database – the Basic Rules
Learn the difference between structured and unstructured data within databases and how machine learning is transforming database usage.
Structured and unstructured data are not an either-or scenario. Instead, it’s a matter of matching the data type to your application and its needs. Each serves a purpose and has advantages and disadvantages.
Get a better idea of when to use structured and unstructured data and some of the benefits of each.
What is Structured Data?
Structured data generally lives within a relational database. Big data is processed using relational databases where each field of information is in length-delimited data. Structured data might include information, such as:
- ZIP codes
- Phone numbers
- Social Security numbers
You can store text fields of various lengths that are easy to search for. These text fields might contain information like a person’s name or attributes.
With structured data, your information can come from human input or be machine-generated. But regardless of where your data is coming from, it has to be in a structured format.
The formatting is what makes it possible for you to search the data or to use algorithms to search for information. Some examples of relational databases that contain structured data include the following.
- Reservation systems
- Inventory databases
- ATM transactions
- Sales data
The main key to the ability to search these databases is that they are formatted in Structured Query Language (SQL).
A relational database can store unstructured data or point to it in another location. One case where you might have structured, and unstructured data is in a customer relationship management system. Memo fields could be unstructured data. Though be aware that this data will not be searchable.
What is Unstructured Data?
Unstructured data refers to everything else. It can have an internal structure of some sort but it is not organized by schema or data models. The data can be textual or not and can come from human entry or be computer-generated.
Unstructured data is stored in a non-relational database in a non-SQL format. Some examples of human-generated data that fit this description include the following.
- Text files, such as Word documents, presentations, logs and spreadsheets
- Email, which is a bit of an anomaly because it can include some structured data thanks to metadata. Email is more of a semi-structured data type where the message itself is unstructured but supported by some structured elements.
- Social media, including Facebook, Instagram or LinkedIn
- Websites, such as sharing sites like YouTube or Flickr
- Mobile data, including text messages
- Communication app data, such as a chat history or phone record
- Media files, including MP3 format or video files
Some data is machine-generated. This might include information from the following sources.
- Satellite imagery that powers weather information or informs military movements
- Scientific information, such as seismic images or space exploration data
- Surveillance photos or video
- Data from sensors, such as traffic or weather data
What’s the Difference Between Structured and Unstructured Data?
The obvious difference is in how and where the data is stored. Unstructured data is generally in a NoSQL database while structured data is in a relational database.
But the difference in the experience with the data is in analytics. Data processing programs can provide much deeper analytics for structured data than you can with unstructured but that’s not to say you will have no analytical capabilities with nonstructured data.
Because nonstructured data is still evolving and developing, there are some tools but they are not as mature as those for nonstructured data.
You can run some simple searches of content within textual unstructured data. However, the lack of order makes it more challenging to mine insights from this data.
Additionally, there are far greater amounts of unstructured data than there is structured data. In fact, unstructured data amounts to about 80 percent of all enterprise data. And, unstructured data grows by about 55-65 percent each year.
Without a way to analyze this unstructured data, enterprises leave behind valuable business intelligence insights. That’s why there is a new emphasis on housing and analyzing unstructured data.
NoSQL databases can process both structured and unstructured data to allow your organization to understand this information and how it pertains to you.
Semi-structured data bridges the gap between structured and unstructured. This type of data includes tags and markings to help identify markers and elements to make the data simpler to analyze, even when it doesn’t fit perfectly into a set structure.
The tags and markings guide analysts to create hierarchies and group the data accordingly. Semi-structured data only accounts for about 5-10 percent of all enterprise data. However, it’s still a crucial element that tells businesses more about their customers and their interactions.
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One of the most common examples of unstructured data is email. Advanced analytics tools can help enterprises search and analyze threads while classifying the data and searching based on keywords.
But that’s just one example of semi-structured data. Many organizations have several sources of semi-structured data. Some other examples include the following.
- XML markup: Is a set of document encoding rules. They define ways for humans and machines to read data using a tag-driven format. The formatting is extremely flexible, and programmers can easily adapt the language to meet their needs.
- NoSQL: Also known as “not only SQL” is another form of unstructured data. Unlike data within relational databases, NoSQL databases do not separate schema from data. NoSQL databases are great for housing information that doesn’t fit perfectly into a table or record format. For example, text fields with variable lengths might not fit within a relational database but would in a NoSQL database. One way to house semi-structured data within a NoSQL database is to store the information in a JSON format natively.
Many NoSQL databases now have analytics tools so that enterprises do not need to separate their data among many databases. These databases are now capable of bringing the data in and providing data scientists with analytic results.
Consider the way LinkedIn works. Millions of people share information about jobs on the platform. The jobs have locations attached to them and required skills. LinkedIn must capture and log all this data in some way. Semi-structured data helps make the data both storable and searchable.
That way, when a job-seeker heads to the platform to look for relevant work, LinkedIn can query the data – though it isn’t perfectly structured – to deliver open job opportunities to the job seeker.
Large retailers with seller networks like Amazon also use semi-structured data to deliver results for products based on queries.
New Tools for Analyzing Unstructured Data Within NoSQL Databases
Machine learning is transforming the industry’s ability to analyze unstructured and semi-structured data. AI enables databases to learn from a user’s decisions and activities to inform future query results.
Emerging tools make it so that unstructured data reacts similarly to structured data, but at much faster speeds with the opportunity to scale quickly. These tools make it possible to analyze sentiments as if they were a black and white piece of data like a ZIP code.
Learn more about how AI is changing NoSQL databases and download BangDB, a NoSQL database that employs machine learning to make unstructured data searchable and usable.