As the Internet age marches forward, we are continuously creating an immeasurable amount of data every second of every day. All that we do online – from purchasing to sending a friend request, performing a Google search, to creating playlists on Spotify – goes on to add to the amount of data being produced. The volume of this data is so vast and ever-increasing that we denote it simply as Big Data.
So much so that we denote this ever-increasing pile of data as Big Data. Naturally, this Big Data presents many opportunities for businesses, analysts, and everyone else to learn many things and improve their processes, techniques, and strategies. As data grew, companies started investing in tools and techniques that could help simplify data and convert it into information. This led to proper characterisation and categorisation of data for ease of analysis. This gave us broadly three categories of data:
This article will look at Structured Data in a Big Data environment!
Also, Let’s dive into the world of big data to know more about types of big data
In the most simple terms, any data that can be accessed, processed, stored, and retrieved in a fixed format, can be termed structured data. As technologies have evolved, it has become more accessible and easier to work with structured data and gather insights.
To define more formally, structured data conforms or pertains to some already existing data model, has a well-defined structure, and follows patterns and orders that help gather insights from it. Structured data can be easily accessed, retrieved, manipulated, and studied by a person or any computer program.
In general, structured data in a Big Data environment is stored in Databases and other well-defined structures and schemas. Structured data has clearly defined attributes for easy access and is tabular, having rows and columns that clearly outline the data structure. Structured Query Language, short for SQL, is primarily the go-to language for communicating with structured data in a Big Data environment.
If you’re still confused as to what is structured data, we’d recommend you to think of structured data as mostly all of your quantitative data like:
- Contact details
- Card details (debit or credit)
- Billing details, etc.
Let’s look at one basic example to give you a better understanding of structured data. Here is a ‘Students’ table in a database that contains their roll numbers, names, genders, classes, and class teacher names.
|1254||A B||Female||1||K L|
|1562||C D||Male||4||M N|
|1768||E F||Female||2||O P|
|1266||G H||Female||7||Q R|
|1980||I J||Male||9||S T|
As you can see, the data in the above table is well-defined, has explicit attributes, and can be accessed in a systematic and structured manner.
Also Read, 5V’s of Big Data
Now, let’s talk about some more practical things about structured data, i.e., where does it come from, and how is it generated?
How is Structured Big Data Generated?
With the evolution of technologies, new ways of structured data generation have evolved that are sophisticated, easier, and more efficient in accessing and analysing. These data sources produce structured data in huge volumes and in real-time. Therefore, the generation of structured Big Data can be attributed to broadly two categories:
- Machine generation of structured data: This is the structured Big Data generated without human intervention. Machines or computers are responsible for the automatic generation of this data.
- Human generation of structured data: This is the data that we, humans, provide by interacting with computers and other digital devices.
There are also hybrid sources that use both machine-generated and human-generated elements, but that can be left for later!
Let’s dive a bit deeper into what machine-generated and human-generated data mean by looking at some examples.
Examples of machine-generated structured Big Data:
- Sensory: Sensory data is produced automatically using sources like smart metres, medical equipment, GPS data, frequency tags, and more. This data is crucial for companies looking to improve their supply chain management.
- Weblog: There are lots of servers, applications, programs running all around the globe at all times. They produce a lot of structured data during their runtime. This amounts to a massive volume of valuable and insightful structured data that companies can use to deal smoothly with SLAs and work proactively on security breaches.
- Point-of-sale: All data generated during point-of-sale activities, including scanning the barcode of all the products, generates lots of structured product-related information.
Examples of human-generated structured Big Data:
- All input data: All of the data we input anywhere on the internet or any digital application adds to the massive pile of Big Data. This data is beneficial for understanding and modifying customer sentiments and behaviour.
- Click-stream: Each click on any website adds to the click-stream data. This can also track, trace, and influence buying behaviour.
- Gaming data: Even the games we play and every in-game purchase and other actions add to the pile of structured Big Data.
- Purchasing actions: All of the activities we make on any social media website, right from looking up the product to making the final purchase – all of it is continuously getting added to Big Data.
To get some perspective on how huge the size of human-generated Big Data is, think that millions of different users submit different information together! Adding to the massive size, the data in real-time makes it ideal for companies looking to make predictions by understanding patterns.
Whatever the mode of data production, the point is that it is incredibly insightful and can solve many business problems.
That explains most of what you need to know about structured data in the Big Data environment. But before we wrap this article up, let’s quickly look at some points of comparison between structured and unstructured data – so that you have some understanding before you dive deeper into unstructured data!
Structured Data vs Unstructured Data
The core difference between the two types of data is the schema and the format it uses for storage and retrieval, influencing what kind of analysis can be drawn from it.
Structured data works with a rigid schema which provides consistency and efficiency. On the other hand, unstructured data has no uniform structure and is inconsistent. For storage, structured data relies on RDBMS and follows a columns-row structure. As this data is well categorised, it can be easily used by both humans and machines. For this, SQL is used, which relies on search queries.
On the other hand, unstructured data either is not organised in a pre-defined manner or does not work with any set data models. This data is generally text-heavy, but sometimes it may also include other information like numbers, dates, etc. Examples of unstructured data may include health records, audio/video/image files, text documents, metadata, books, analogue data, emails, etc.
More often than not, you will find structured and unstructured data being used together, more often than not. For instance – a CRM system (unstructured data) could be producing an excel sheet of company data (structured data).
Structured data is constantly being made rapidly, which will only increase with time. As a result, companies have to deal with heaps of data that hold vital information and potential to help the company reach its goals. Knowing how to extract knowledge from data is one of the key skills of now and the future.
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1. What are the three types of data in a big data environment?
Structured, Unstructured, and Semi-structured are the three broad categories of data.
2. How is structured data studied and analyzed?
Since structured data is stored in a table format, row-column structure, it can be accessed using Structured Query Language. This is one of the essential languages to learn if you want to begin your journey in Big Data.
3. What are the advantages of structured data?
Apart from being relatively easy to use by humans, structured data can also be easily used by ML algorithms. This makes it extremely useful for gathering insights in an automated and quick manner.