3 hours 51 minutes
Welcome back to Microsoft Azure Fundamentals. This is Module seven. As your storage services
in this module, we'll learn about the different types of data that companies collect and store in the cloud
the various services azure offers for storing the data
and how storing data in the cloud will benefit you versus storing it on premises.
Let's get started
before we look at azure data storage options. Let's first see what types of data enterprises collect.
Although enterprises have been collecting data for decades now,
the majority of data in the past has been stored in the so called structured format.
Structured data conforms to a pretty fine schema, and it is stored in tables with rows and columns.
Before starting the data into tables, the data is normalized to reduce the amount of information stored in the tables and to improve its querying capabilities.
During the normalization, the data is split into subsets and stored in separate tables.
Those tables are linked with the help of keys to indicate the relationship between rose and separate tables.
That is why structured data is also referred to as relational data.
Each column in the table can store a specific data type like an integer number, a floating number or a string.
The column data types are enforced and an error is thrown if you try to write a different type of data into any given column.
Typical examples of structured data are all the data shown in the example.
Other financial data, like bank ledgers or sensor data.
Semi structured data is another type of data that has become prevalent recently.
Semi structured data doesn't really fit into tables and doesn't have a predetermined schema.
Instead, semi structured data uses keys to organize the data and provide hierarchy.
Typical examples of semi structured data are key value data where both keys and values can be of any type numbers, strings or objects.
Key value data can be represented in a simple table with two columns, a key column and a value column,
and each new entry is stored as a new role in the table.
there are no restrictions on the types of data stored in any of the columns.
Another example of semi structured data is object data, where each object has an identified here. But the content can vary from object to object.
Jason objects are a perfect example of this.
In this example, we have a customer object that contains certain information about the customer.
However, this object can be updated if new information about the customer is discovered.
There is no schema that restricts you on adding or removing information from the object.
The third type of data is called unstructured data.
Unstructured data could be anything.
There are no restrictions on format or structure.
It could be a PDF file, a word document, a video or picture, a text file or any binary block.
Even the Rogue Jason or database files could be considered unstructured data.
Very often, you can attach metadata to the unstructured data to provide some classification to it.
We already generate a lot of data today.
Tweets, Facebook posts and instagram images,
monitoring analytics, data security data sensor data and whatever else you can imagine.
The expectation is that by 2020 we will generate 1.7 megabytes per second for every person on the planet.
The term for that data is big data.
Gartner defines big data as the three V s
high volume, high velocity and high variety information assets that demand cost effective, innovative forms of information processing that enable enhanced insight and decision making and process automation.
In simple terms.
Big data is data that arrives in high volumes very often and can be in any form
in the next video. We'll see how azure helps you store any kind of data.
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