Data Structure 90% of the world's data has been captured in the last two years.

Data can take lots of forms. It can be words, numbers images, video, even social media posts.

And data can be categorized as structured or unstructured.

Structured and unstructured data is sourced collected and scaled in different ways.

Structured data would typically be values such as names, addresses, transaction data, amounts, or credit card numbers.

Unstructured data would often contain values such as social media tweets, emails, car sensor data, even satellite images or audio and video files.

Structured data is always stored in rows and columns.

It's really easy to search.

And it's often quantitative data that it's using so that would mean things like numbers and text.

Typically, we would use Excel, SQL, or even Azure to hold structured data.

On a structured dataset we'll find tables and databases.

So for example an Excel spreadsheet holds table of information.

That table is divided into rows and columns Excel has a 1 million limit to the number of rows other systems and databases would hold much more. So we'll find that our first row in an Excel spreadsheet for example would contain the column names. These are known as fields in databases and tables.

Tables like this are really easy to search through to filter and to organize we'll often find that one column of information will hold unique values.

So each row can be easily identified using that value.

Unstructured data is much more undefined.

So it's quite difficult to search through and process on structured data as it holds typically qualitative information. So this would be quite text heavy and descriptive data.

It has to be often stored in special data lakes or non-relational databases.

And it does take up a lot of storage space.