Data is the foundation of your business. It's how you make decisions, it's how you build your product, and it's how you get feedback on what you're doing.

Your data can tell if your team is hitting its goals if users are having trouble with certain features, and what products people want to buy.

Data-driven decision-making means focusing on the things that matter instead of guessing or wasting time on ideas that don't work.

What Is Structured Data?

Structured data is the most common way of storing and processing information. It is data that has been organized into a database that can be easily queried and analyzed. It is because the data are formatted in a way that they are structured and have relational keys that allow them to be easily mapped into pre-designed fields.

Structured data is typically used by developers in the development stage of an application, as it's the most straightforward way to manage information. When non-developers take over, they often need help with how best to use this data type. Examples of structured data include relational data and XML documents.

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What Is Unstructured Data?

Unstructured data is information that lives outside of traditional databases and spreadsheets. It's often text-based. 

But it can also include images, audio, video, and other forms of media. It's called unstructured because it doesn't fit into neat rows and columns like traditional databases.

Unlike structured data, unstructured data usually contains more information about an individual or company than their name and address.

What Is Semi-Structured Data?

Semi-structured data is a type of data that doesn't have a rigid structure. It's more like unstructured data, but it does have some sort of order, so it's not unstructured.

It can be found in many places, including social media posts and emails. The reason it's called semi-structured is that the data has some structure but less than structured data.

Semi-structured data is often stored in databases or spreadsheets and analyzed with tools like Hadoop or NoSQL databases.

Structured vs. Unstructured Data


Structured data

Unstructured data


Structured data is stored in a relational database

It is based on the conventions of character sets consisting of binary data strings


It is dependent on your schema, that is, the way you perceive and interpret the world around you

It is easier to use than a schema and more flexible


Scaling the database schema is a significant challenge

Highly scalable


Highly robust

Not as robust as RDBMS


Performance is faster in this case because the query is structured, allowing more efficient joining

When dealing with unstructured data, textual queries are possible but less effective than with structured or semi-structured data


Quantitative data is any data that can be counted, such as numbers or percentages

It is not quantifiable, as it cannot be processed or analyzed similarly to structured data


It has a set structure

It is available in a wide range of sizes and shapes


Searching is fast and convenient

Finding unstructured data makes finding the information you are looking for challenging


Structured data is easy to read, understand, and process. It doesn't require additional processing to be understood and used by a machine

Unstructured data is more easily accessible than structured data


It is not flexible or rigid to maintain

Unstructured data is messy and difficult to manage, especially if you have a lot of it. There can be a lot of duplicates or missing information




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Key Differences Between Structured and Unstructured Data

Structured and unstructured data are two different types of data that are used in different ways. Structured data can be organized into columns, rows, and tables. A computer program can also manipulate it to perform calculations, such as adding up the sales of all the products sold in a store. 

Unstructured data doesn't have a set structure; it's just text, images, or other information that doesn't fit into rows and columns.

Structured data is often used by businesses to make business decisions. For example, if you're running a clothing store and want to know how many people bought your new line of shoes last month, you could automatically use structured data to calculate this number for you!

Unstructured data is often used for analysis purposes. For example, if you wanted to see what pictures people were posting on Instagram about your company's product line last year, you could use unstructured data to find those pictures for yourself!

The Future of Data

Data is ubiquitous, and it's only getting more so. Whether you're looking at a car or a phone, a building or an airport, they all have a data collection system. And that's just what we know. Countless other systems collect data that are entirely invisible to us.

As we continue to collect, store, and analyze data in more and more ways, we'll have access to more insight into our world than ever before. We'll be able to see trends and predict what will happen next with a higher degree of accuracy.

We can use this information to help us make better decisions about the world around us—for example, by using it to predict severe weather patterns so that we can prepare for them. We can also use it to ensure that our cities are designed sustainably and environmentally friendly.

The possibilities are endless!

Our Professional Certificate Program in Data Engineering is delivered via live sessions, industry projects, masterclasses, IBM hackathons, and Ask Me Anything sessions and so much more. If you wish to advance your data engineering career, enroll right away!


Data Engineering is the hot new thing in the IT industry.

It's not just about being able to store and retrieve data but about being able to do it quickly, efficiently, and with a minimum of human interaction.

Simplilearn's Data Engineering Certification Course, in partnership with Purdue University & IBM, will help you master crucial Data Engineering skills.

It's applied learning program will help you get up-to-date with data engineering to stay on top of the latest trends in this rapidly growing field.


1. What Is Structured Data?

Structured data is information organized in a way that makes it easy for machines to read, understand, and process.

2. Structured Vs. Unstructured Data: What’s The Difference?

Structured and unstructured data are two different types of data, each with unique characteristics.

Structured data is information that has been organized consistently. This type of data is typically stored in tables, databases, or spreadsheets and can be queried using standard search techniques. Unstructured data is information that has not been structured in any way. This type of data can be challenging to analyze because it can be arranged in many different ways and has no apparent order or structure.

Unstructured data contains emails, documents, social media posts, images, and videos. Structured data includes things like customer information stored in an online database or financial reports stored on a spreadsheet program like Excel.

3. What are structured and unstructured data examples?

Structured data can be easily organized and analyzed using a computer. It includes things like spreadsheets, databases, and other standard formats.

Unstructured data cannot be easily managed and analyzed with a computer. It can include images, text messages, audio files, and more.

4. Is Excel structured or unstructured data?

Excel is structured data.

Data is structured when it has been given a specific format and meaning. The column numbers in an Excel spreadsheet are structured because they have been given a particular form, and the columns represent different types of data that can be sorted, compared, and analyzed.

5. What is an example of unstructured data?

Unstructured data refers to any data that doesn't fit into a predefined set of categories. It includes things like photos and videos.

6. How do I know if my data is structured or unstructured?

To know if your data is structured or unstructured, you must first define what that means. Structured data is typically organized into columns and rows, such as in a spreadsheet or database. Unstructured data is typically text-based and can be stored in various formats without specific organization.

Our Big Data Courses Duration And Fees

Big Data Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Post Graduate Program in Data Engineering

Cohort Starts: 25 Apr, 2024

8 Months$ 3,850