Welcome to this course on Azure Data Fundamentals! This course is designed for anyone who wants to learn the fundamentals of database concepts in a cloud environment, get basic skilling in cloud data services, and build their foundational knowledge of cloud data services within Microsoft Azure. The course provides a practical, hands-on approach in which you will get a chance to see data in action and try Azure data services for yourself.

Audience

Candidates for this exam should have a foundational knowledge of core data concepts and how they are implemented using Microsoft Azure data services. This exam is intended for candidates beginning to work with data in the cloud. Candidates should be familiar with the concepts of relational and non-relational data, and different types of data workloads such as transactional or analytical.

DP-900 Skills Measured

The exam includes four study areas. The percentages indicate the relative weight of each area on the exam. The higher the percentage, the more questions the exam will contain.

  • Describe core data concepts (15-20%)
  • Describe how to work with relational data on Azure (25-30%)
  • Describe how to work with non-relational data on Azure (25-30%)
  • Describe an analytics workload on Azure (25-30%)

You can access the free on-demand training for DP-900 below

Pre-requisites

Candidates should be familiar with the concepts of relational and non-relational data, and different types of data workloads such as transactional or analytical. Azure Data Fundamentals can be used to prepare for other Azure role-based certifications like Azure Database Administrator Associate or Azure Data Engineer Associate, but it’s not a prerequisite for any of them.

Module 1 Explore core data concepts

In this module, you will explore relational data offerings, provisioning and deploying relational databases, and querying relational data through cloud data solutions with Microsoft Azure.

Explore core data concepts

Data is a collection of facts such as numbers, descriptions, and observations used in decision making. You can classify data as structured, semi-structured, or unstructured. Structured data is typically tabular data that is represented by rows and columns in a database. Databases that hold tables in this form are called relational databases.

Explore roles and responsibilities in the world of data

Over the last decade, the amount of data that systems and devices generate has increased significantly. Because of this increase, new technologies, roles, and approaches to working with data are affecting data professionals. Data professionals typically fulfill different roles when managing, using, and controlling data. In this module, you’ll learn about the various roles that organizations often apply to data professionals and the tasks and responsibilities associated with these roles.

There are three key job roles that deal with data in most organizations. Database Administrators manage databases, assigning permissions to users, storing backup copies of data, and restore data in case of any failures. Data Engineers are vital in working with data, applying data cleaning routines, identifying business rules, and turning data into useful information. Data Analysts explore and analyze data to create visualizations and charts to enable organizations to make informed decisions.

Describe concepts of relational data

The simple yet powerful relational model is used by organizations of all types and sizes for a broad variety of information management needs. Relational databases are used to track inventories, process e-commerce transactions, manage huge amounts of mission-critical customer information, and much more. A relational database is useful for storing any information containing related data elements that must be organized in a rules-based, consistent way.

Creating a relational database model for a large organization is not a trivial task. It can take several iterations to define tables to match the characteristics described above. Sometimes you have to split an entity into more than one table. This process is called normalization.

Explore concepts of non-relational data

Relational databases are an excellent tool for storing and retrieving data that has a well-known structure, containing fields that you can define in advance. In some situations, you might not have the required knowledge of the structure of your data, in advance of it arriving in your database, to record it as a neat set of rows and columns in a tabular format. This is a common scenario in systems that consume data from a wide variety of sources, such as data ingestion pipelines. In these situations, a non-relational database can prove extremely useful.

Explore concepts of data analytics

Data analytics is concerned with taking the data that your organization produces and using it to establish a picture of how your organization is performing, and what you can do to maintain business performance. Data analytics helps you to identify strengths and weaknesses in your organization and enable you to make appropriate business decisions.

Module 01 Knowledge check

In this module, we explored relational data offerings, provisioning and deploying relational databases, and querying relational data through cloud data solutions with Microsoft Azure.

Module 2 Explore relational data in Azure

In this module, you will explore relational data offerings, provisioning and deploying relational databases, and querying relational data through cloud data solutions with Azure.

Explore relational data offerings in Azure

Azure offers a range of options for running a database management system in the cloud. For example, you can migrate your on-premises systems to a collection of Azure virtual machines. This approach still requires that you manage your DBMS carefully. Alternatively, you can take advantage of the various Azure relational data services available. These data services manage the DBMS for you, leaving you free to concentrate on the data they contain and the applications that use them.

Azure Data Services fall into the PaaS category. These services are a series of DBMSs managed by Microsoft in the cloud. Each data service takes care of the configuration, day-to-day management, software updates, and security of the databases that it hosts. All you do is create your databases under the control of the data service. Azure Data Services are available for several common relational database management systems. The most well-known service is the Azure SQL Database. The others currently available are Azure Database for MySQL servers, Azure Database for MariaDB servers, and Azure Database for PostgreSQL servers.

Deploying relational database offerings in Azure

Azure supports a number of database services, enabling you to run popular database management systems, such as SQL Server, PostgreSQL, and MySQL, in the cloud. Azure database services are fully managed, freeing up valuable time you’d otherwise spend managing your database. Enterprise-grade performance with built-in high availability means you can scale quickly and reach global distribution without worrying about costly downtime. Developers can take advantage of industry-leading innovations such as built-in security with automatic monitoring and threat detection, automatic tuning for improved performance. On top of all of these features, you have guaranteed availability.

Query relational data in Azure

Azure enables you to create relational databases using a number of technologies, including Azure SQL Database, Azure Database for PostgreSQL, Azure Database for MySQL, and Azure Database for MariaDB. Imagine that you work as a developer for a large supermarket chain called Contoso. The company has created a data store that will be used to store product inventory. The development team has used an Azure SQL database to store its data. They need to know how to query and manipulate this data using SQL. In this lesson, you’ll learn how to use these database services to store and retrieve data. You’ll examine how to use some of the common tools available for these database management systems to connect to database services running in Azure.

Module 2 Knowledge check

In this module, we explored relational data offerings, provisioning and deploying relational databases, and querying relational data through cloud data solutions with Azure.

Module 3 Explore non-relational data offerings on Azure

In this module, you will explore non-relational data offerings, provisioning and deploying non-relational databases, and non-relational data stores with Microsoft Azure.

Explore non-relational data offerings in Azure

Data comes in all shapes and sizes, and can be used for a large number of purposes. Many organizations use relational databases to store this data. However, the relational model might not be the most appropriate schema. The structure of the data might be too varied to easily model as a set of relational tables. For example, the data might contain items such as video, audio, images, temporal information, large volumes of free text, encrypted information, or other types of data that aren’t inherently relational. Additionally, the data processing requirements might not be best suited by attempting to convert this data into the relational format. In these situations, it may be better to use non-relational repositories that can store data in its original format, but that allow fast storage and retrieval access to this data.

Deploying non-relational data services in Azure

Microsoft Azure supports a number of non-relational data services, including Azure File storage, Azure Blob storage, Azure Data Lake Store, and Azure Cosmos DB. These services support different types of non-relational data. For example, you can use Cosmos DB to store documents, and Blob storage as a repository for large binary objects such as video and audio data. Before you can use a service, you must provision an instance of that service. You can then configure the service to enable you to store and retrieve data, and to make it accessible to the users and applications that require it.

Manage non-relational data stores in Azure

Non-relational data stores can take many forms. Azure enables you to create non-relational databases using Azure Cosmos DB. Cosmos DB supports several NoSQL models, including document stores, graph databases, key-value stores, and column-family databases. Other non -relational stores available in Azure include Azure Storage, which you can use to store blobs and files. In this lesson, you’ll learn how to use these various storage services to store and retrieve data.

Module 3 Knowledge check

In this module, we explored non-relational data offerings, provisioning and deploying non-relational databases, and non-relational data stores with Microsoft Azure.

Module 4: Explore modern data warehouse analytics in Azure

In this module, you will explore the processing options available for building data analytics solutions in Azure. You will explore Azure Synapse Analytics, Azure Databricks, and Azure HDInsight. You’ll Learn what Power BI is, including its building blocks and how they work together.

Components of a modern data warehouse

Most organizations have multiple data stores, often with different structures and varying formats. They often have live, incoming streams of data, such as sensor data, that can be expensive to analyze. There’s often a plethora of useful information available outside of organizations. This information could be combined with local data to add insights and enrich understanding. By combining all local data with useful external information, it’s often possible to gain insights into the data that weren’t previously possible. The process of combining all of the local data sources is known as data warehousing. The process of analyzing streaming data and data from the Internet is known as Big Data analytics. Azure Synapse Analytics combines data warehousing with Big Data analytics.

Explore data ingestion in Azure

Data ingestion is the process used to load data from one or more sources into a data store. Once ingested, the data becomes available for use. Data can be ingested using batch processing or streaming, depending on the nature of the data source. Organizations often have numerous, disparate data sources. To deliver a full cloud solution, it’s important to have a flexible approach to data ingestion into an Azure data store. Azure offers many ways to ingest data. In this lesson, you’ll explore some of these tools and techniques that you can use to ingest data with Azure.

Explore data storage and processing in Azure

Organizations generate data throughout their business. For analysis purposes, this data can be left in its raw, ingested format, or the data can be processed and saved to a specially designed data store or data warehouse. Azure enables businesses to implement either of these scenarios. The most common options for processing data in Azure include Azure Databricks, Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake. In this unit, you’ll explore these options in more detail.

Get started building with Power BI

Microsoft Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Whether your data is a simple Microsoft Excel workbook or a collection of cloud-based and on-premises hybrid data warehouses, Power BI lets you easily connect to your data sources, visualize (or discover) what’s important, and share that with anyone or everyone you want.

Module 4 Knowledge check

In this module, we explored the processing options available for building data analytics solutions in Azure. We explored Azure Synapse Analytics, Azure Databricks, and Azure HDInsight. We Learnt what Power BI is, including its building blocks and how they work together.

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Cheers!

Susanth Sutheesh

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