Microsoft Azure Data Fundamentals Course Training and Certification

The Microsoft Azure Data Fundamentals Course Training and Certification is a great way to get started with data management on the Azure platform. This course is designed to help you understand the basics of data storage, security, and analytics on Azure.

You'll learn about the core services that make up the Azure Data Platform, including Azure Storage, Azure SQL Database, and Azure Synapse Analytics. These services are the foundation of data management on Azure, and understanding how they work together is crucial for success.

The course is divided into several modules, each focusing on a specific aspect of data management on Azure. You'll learn how to create and manage storage accounts, configure database security, and analyze data using Azure Synapse Analytics. By the end of the course, you'll have a solid understanding of the Azure Data Platform and be able to implement data management solutions in real-world scenarios.

Data Fundamentals is a crucial part of any data-related course, and the Microsoft Azure Data Fundamentals course is no exception. You'll learn about core data concepts, including core data workloads and data analytics core concepts.

In this course, you'll explore the characteristics of transactional and analytical data processing solutions, and describe the differences between them. You'll also learn about the characteristics of relational and non-relational data, and identify common data formats.

Here are some key data fundamentals concepts you'll cover in the course:

By mastering these data fundamentals concepts, you'll be well on your way to becoming proficient in working with data on Azure.

To master the fundamentals of data, you'll need to demonstrate certain skills.

You'll be able to describe core data concepts, such as understanding the differences between relational and non-relational data on Azure.

To work with relational data on Azure, you'll need to consider factors like data modeling and schema design.

You'll also need to identify considerations for working with non-relational data on Azure, such as choosing the right data model for your needs.

Describe an analytics workload on Azure, including understanding the types of workloads and their requirements.

Here are the core skills you'll need to demonstrate:

Core data concepts are the foundation of understanding data fundamentals. Data is represented and used in various ways, including common data formats such as structured, semi-structured, and unstructured data.

Structured data has a defined format, making it easy to store and retrieve. Semi-structured data has a partially defined format, while unstructured data has no defined format. This is important to understand when working with different types of data.

Relational data is a common way to store and manage transactional and analytical data. It has characteristics such as tables, rows, and columns, which are used to organize and store data. Normalization is a process used to organize data in a relational database to minimize data redundancy.

Relational data can be represented in various ways, including SQL statements. SQL statements are used to manage and retrieve data from relational databases. Everyday relational database objects include tables, views, and indexes.

Data workloads are classified into two main categories: transactional and analytical. Transactional workloads involve processing large amounts of data in real-time, while analytical workloads involve processing data for reporting and analysis.

Here are the core data concepts summarized:

Microsoft Azure offers a range of database and storage services to meet various needs. Relational databases, such as Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines, are available for structured data.

Azure Storage provides options for non-relational data, including Azure Blob storage, Azure File storage, and Azure Table storage. These services allow for scalable and secure storage of unstructured and semi-structured data.

The following services are available for database and storage in Azure: Azure SQL Database, Azure SQL Managed Instance, SQL Server on Azure Virtual Machines, Azure Blob storage, Azure File storage, Azure Table storage, and Azure Cosmos DB. These services can be used for a variety of use cases, including big data analytics and IoT data processing.

Here is a list of some key database and storage services offered by Azure:

When choosing a data storage format, you have several options to consider.

One common format for data files is the CSV (Comma Separated Values) format.

You can also consider using a database for storing and managing your data.

There are two main types of databases: relational databases and open-source databases.

Here are some options for relational databases:

If you're looking to provision a database service on Azure, you can choose from a variety of options and services.

Database is a crucial component of modern computing, and understanding its concepts is essential for anyone working with data. Relational databases are a type of database that stores data in tables with defined relationships between them.

Normalization is a process used in relational databases to organize data in a way that minimizes data redundancy and dependency. This is done by dividing large tables into smaller ones, making it easier to manage and maintain the data.

There are several types of relational databases, including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines. Each of these options has its own set of features and capabilities.

Azure provides several relational database management systems, including Azure SQL and open-source database systems. You can choose the best relational database management system and host relational data in the cloud.

Relational databases in Azure offer a range of features, including support for structured query language (SQL) statements. Some common SQL statements include SELECT, INSERT, UPDATE, and DELETE.

Here are some common database objects:

Relational databases in Azure also provide management tasks, such as creating and managing databases, users, and permissions. Additionally, they offer query techniques, such as indexing and joining tables.

Some common database features include:

The following are some key features of Azure Cosmos DB:

Azure Cosmos DB is a NoSQL database service that provides a flexible and scalable way to store and manage large amounts of data. It supports multiple APIs, including SQL, MongoDB, Cassandra, and Gremlin.

Here are some common use cases for Azure Cosmos DB:

In this course, you'll learn the fundamentals of data visualization and large-scale data warehousing using Microsoft Azure. You'll be able to describe a high-level process for creating reporting solutions with Microsoft Power BI.

To get started with data visualization, you'll need to identify common types of data visualization and their uses. This includes understanding the core principles of analytical data modeling and how to create an interactive report with Power BI Desktop.

Here are some key takeaways from the course:

By learning these skills, you'll be able to build a solid foundation in data visualization and large-scale data warehousing, setting yourself up for success in your career.

In large-scale analytics, data ingestion and processing are critical considerations. You need to think about how to handle massive amounts of data efficiently.

Data workloads come in two main flavors: transactional and analytical. Transactional workloads are all about processing individual transactions, while analytical workloads focus on analyzing large datasets.

Transactional workloads typically involve features like ACID compliance and high throughput, whereas analytical workloads require features like high scalability and data aggregation.

Here are some common features of analytical workloads:

In a modern data warehousing solution, you'll often find a combination of data ingestion pipelines and analytical data stores. Azure Synapse Analytics is a great example of an analytical data store that can handle large-scale data processing and querying.

In this section, we'll dive into the world of visualization, where data comes to life in a way that's easy to understand and interact with.

Data visualization is a crucial part of analytics, as it helps us make sense of complex data by presenting it in a clear and concise manner.

The core principles of analytical data modeling, which we'll cover in Module 9, play a key role in creating effective data visualizations.

By understanding these principles, you'll be able to identify common types of data visualization and their uses.

Here are some common types of data visualization:

With Microsoft Power BI as a platform, you'll be able to create an interactive report that brings your data to life.

By following the high-level process for creating reporting solutions with Power BI, you'll be able to describe the core principles of analytical data modeling and identify common types of data visualization.