Structured Semi Structured and Unstructured DATA Storage Design Microsoft Azure Architect Differenc
In this video, I explain the difference between the Data storage concepts in Azure Cloud and why everyone should understand, and the importance of Designing Azure Solutions for your cloud application. What's the difference between structured semi structured and unstructured data? tutorial for beginners What is the difference between structured data and unstructured data? What is the difference between semi structured and structured Structured Vs Unstructured Data Vs Semi-Structured Data Understanding Structured, Unstructured & Semi-Structured Data Structured Vs. Semi-Structured Vs. Unstructured Data Structured vs. Unstructured Data: What's the Difference? Structured vs Unstructured vs Semi-Structured Data The Difference Between Structured and Unstructured Data What is structured data vs unstructured data? Semistructured Data - an overview Data types and applications: Structured vs unstructured data Differences between Structured, Semi-structured and unstructured What Is Structured Data? What Is Unstructured Data? Azure provides a variety of ways to store data: unstructured, archival, relational, and more. In this course, you will learn the basics of storage management in Azure cloud, how to create a Storage Account, and how to choose the right model for the data you want to store in the cloud. This lecture is part of Exam AZ-305 Designing Microsoft Azure Infrastructure Solutions course. #az305 #azure Each type of data may benefit from a different storage solution. Application data can be classified in one of three ways. Structured, semi-structured, and unstructured. Let's start by looking at structured data. Structured data, sometimes referred to as relational data, is data that adheres to a strict schema. All of the data has the same fields or properties. The shared schema allows this type of data to be easily searched with query languages such as SQL or structured query language. This capability makes this data style perfect for applications such CRM systems, reservations, and inventory management. Structured data is often stored in database tables with rows and columns with key columns to indicate how one row in a table relates to data in another row of another table. Semi-structured data: This data is less organized than structured data and is not stored in a relational format, as the fields do not neatly fit into tables, rows and columns. Semi-structured data contains tags that make the organization and hierarchy of the data apparent. For example, key-value pairs. Semi-structured data is also referred to as non-relational or NoSQL data. The expression and structure of the data in this style is defined by a serialization language. Data serialization languages are an effective way to exchange semi-structured data across systems. Using standardized well-documented syntax, these languages allow data to be shared across systems with different underlying infrastructure, such as between a mobile device and a website. For software developers, data serialization languages are particularly important because they can be used to write data stored in memory to a file, then sent to another system, and then parsed and read in again. These languages include JSON or JavaScript Object Notation, XML or Extensible Markup Language, and YAML or YAML Ain't Markup Language. XML was one of the first data languages to receive widespread support. Its tech space, which makes it easily human and machine readable. In addition, parsers for it can be found in almost all popular development platforms. XML allows you to express relationships and has standards for schema, transformation and even displaying on the web. Let's review an example of a person with hobbies expressed in XML. XML expresses the shape of the data using tags. These tags come in two forms, elements such as firstName and attributes that can be expressed in texts like age equals 23. Elements can have child elements to express relationships. YAML is a relatively new data language that's growing quickly in popularity in part due to its human friendliness. The data structure is defined by line separation and indentation and reduces the dependency on structural characters like parentheses, commas, and brackets. Let's look at the earlier example of a person with hobbies, this time expressed in YAML. This format is more readable than JSON and is often used for configuration files that need to be written by people but parsed by programs. However, YAML is the newest of these data formats and doesn't have as much support in programming languages as JSON and XML. structured data is organized data that neatly fits into rows and columns in tables. Semi-structured data is still organized and has clear properties and values, but there's variety to the data. Unstructured data doesn't fit neatly into tables, nor does it have a schema. #paddymaddy #microsoftazure
In this video, I explain the difference between the Data storage concepts in Azure Cloud and why everyone should understand, and the importance of Designing Azure Solutions for your cloud application. What's the difference between structured semi structured and unstructured data? tutorial for beginners What is the difference between structured data and unstructured data? What is the difference between semi structured and structured Structured Vs Unstructured Data Vs Semi-Structured Data Understanding Structured, Unstructured & Semi-Structured Data Structured Vs. Semi-Structured Vs. Unstructured Data Structured vs. Unstructured Data: What's the Difference? Structured vs Unstructured vs Semi-Structured Data The Difference Between Structured and Unstructured Data What is structured data vs unstructured data? Semistructured Data - an overview Data types and applications: Structured vs unstructured data Differences between Structured, Semi-structured and unstructured What Is Structured Data? What Is Unstructured Data? Azure provides a variety of ways to store data: unstructured, archival, relational, and more. In this course, you will learn the basics of storage management in Azure cloud, how to create a Storage Account, and how to choose the right model for the data you want to store in the cloud. This lecture is part of Exam AZ-305 Designing Microsoft Azure Infrastructure Solutions course. #az305 #azure Each type of data may benefit from a different storage solution. Application data can be classified in one of three ways. Structured, semi-structured, and unstructured. Let's start by looking at structured data. Structured data, sometimes referred to as relational data, is data that adheres to a strict schema. All of the data has the same fields or properties. The shared schema allows this type of data to be easily searched with query languages such as SQL or structured query language. This capability makes this data style perfect for applications such CRM systems, reservations, and inventory management. Structured data is often stored in database tables with rows and columns with key columns to indicate how one row in a table relates to data in another row of another table. Semi-structured data: This data is less organized than structured data and is not stored in a relational format, as the fields do not neatly fit into tables, rows and columns. Semi-structured data contains tags that make the organization and hierarchy of the data apparent. For example, key-value pairs. Semi-structured data is also referred to as non-relational or NoSQL data. The expression and structure of the data in this style is defined by a serialization language. Data serialization languages are an effective way to exchange semi-structured data across systems. Using standardized well-documented syntax, these languages allow data to be shared across systems with different underlying infrastructure, such as between a mobile device and a website. For software developers, data serialization languages are particularly important because they can be used to write data stored in memory to a file, then sent to another system, and then parsed and read in again. These languages include JSON or JavaScript Object Notation, XML or Extensible Markup Language, and YAML or YAML Ain't Markup Language. XML was one of the first data languages to receive widespread support. Its tech space, which makes it easily human and machine readable. In addition, parsers for it can be found in almost all popular development platforms. XML allows you to express relationships and has standards for schema, transformation and even displaying on the web. Let's review an example of a person with hobbies expressed in XML. XML expresses the shape of the data using tags. These tags come in two forms, elements such as firstName and attributes that can be expressed in texts like age equals 23. Elements can have child elements to express relationships. YAML is a relatively new data language that's growing quickly in popularity in part due to its human friendliness. The data structure is defined by line separation and indentation and reduces the dependency on structural characters like parentheses, commas, and brackets. Let's look at the earlier example of a person with hobbies, this time expressed in YAML. This format is more readable than JSON and is often used for configuration files that need to be written by people but parsed by programs. However, YAML is the newest of these data formats and doesn't have as much support in programming languages as JSON and XML. structured data is organized data that neatly fits into rows and columns in tables. Semi-structured data is still organized and has clear properties and values, but there's variety to the data. Unstructured data doesn't fit neatly into tables, nor does it have a schema. #paddymaddy #microsoftazure