Azure Data Fundamentals Certification: A Comprehensive Guide

The Azure Data Fundamentals Certification is a great starting point for anyone looking to get into the world of data and analytics on Azure. It covers the basics of data storage, data movement, and data processing.

To pass the certification, you'll need to have a good understanding of Azure services such as Azure Storage, Azure Data Factory, and Azure Databricks. These services are all covered in the certification exam.

The certification is designed for data professionals who are new to Azure or looking to transition into a data role. It's a great way to demonstrate your skills and knowledge to potential employers.

The target audience for the Azure Data Fundamentals certification is individuals who have a good understanding of core data concepts and related Microsoft Azure data services. They should have familiarity with the exam's learning material, whether it's self-paced or instructor-led.

This exam is an opportunity to demonstrate knowledge of relational and non-relational data, as well as different types of data workloads like transactional or analytical.

To prepare for this exam, candidates should have a solid grasp of these concepts.

The Azure Data Fundamentals certification is a great starting point for those looking to pursue other Azure role-based certifications, such as Azure Database Administrator Associate or Azure Data Engineer Associate. However, it's not a prerequisite for any of these certifications.

Relational databases are a fundamental concept in Azure data fundamentals certification. They are a type of database that stores data in tables with well-defined relationships between them.

To understand relational databases, you need to know their features, which include data normalization, data integrity, and support for transactions. Relational databases also offer a range of management tasks, such as backups, high availability, and security.

In Azure, relational databases are offered through various services, including Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines. These services provide a range of benefits, including scalability, high availability, and security.

To manage relational databases in Azure, you need to be familiar with query techniques for SQL, including SELECT, INSERT, UPDATE, and DELETE statements. You should also know how to identify common database objects, such as tables, views, and stored procedures.

Here are some key features of relational databases in Azure:

Relational databases are used to store and manage data in a structured and organized way. They are commonly used in applications that require complex transactions and data relationships.

In the world of Azure, non-relational databases are an essential tool for handling large amounts of unstructured or semi-structured data. Non-relational databases, also known as NoSQL databases, are designed to scale horizontally and handle high volumes of data.

Azure offers a robust non-relational database service called Azure Cosmos DB. This service allows you to store and manage large amounts of data across multiple regions and partitions.

For effective management of Azure Cosmos DB, you'll need to perform various tasks such as monitoring, backup, and restore. These tasks are essential for ensuring the high availability and performance of your database.

Here's a quick rundown of the key features and management tasks for Azure Cosmos DB:

Relational Databases are a fundamental concept in database management. They use tables to store data, where each table has rows and columns.

In Azure, relational databases have various features, including relational database offerings, management tasks, and query techniques for SQL. These features make it easier to manage and query data in relational databases.

One of the key benefits of relational databases is normalization, which is used to reduce data redundancy and improve data integrity. Normalization involves organizing data into tables with each table containing a unique set of values.

Some common structured query language (SQL) statements include SELECT, INSERT, UPDATE, and DELETE. These statements are used to manipulate data in relational databases.

Relational databases also have common database objects, such as tables, indexes, and views. These objects are used to organize and manage data in relational databases.

Here are some examples of relational Azure data services:

In contrast to relational databases, non-relational databases, such as NoSQL databases, do not use tables to store data. Instead, they use a variety of data models, such as key-value, document, and graph databases.

Nonrelational Databases are a type of database that stores data in a flexible and scalable way. They are designed to handle large amounts of unstructured or semi-structured data, making them ideal for big data applications.

Azure offers a nonrelational database service called Azure Cosmos DB, which provides a globally distributed database with high availability and consistency. It's a great option for developers who need to build scalable and performant applications.

One of the key features of nonrelational databases is their ability to handle high traffic and large amounts of data. This is achieved through the use of distributed storage and scalable architecture.

Management tasks for Azure Cosmos DB include creating and managing databases, collections, and documents. This can be done through the Azure portal, Azure CLI, or Azure SDKs.

Here are some common management tasks for Azure Cosmos DB: