Hello and welcome to Microsoft Azure Data Fundamentals. The focus of this learning path is to prepare you for Microsoft’s DP-900 exam.

My name’s Guy Hummel and I’m a Microsoft Certified Azure Solutions Architect and Data Engineer.

The DP-900 exam tests your knowledge of four subject areas: core data concepts, working with relational data on Azure, working with non-relational data on Azure, and analytics workloads on Azure. I’ll go over what you need to know for each section in the exam guide, but be aware that this learning path covers the topics in a slightly different order. We did it this way because the exam guide is designed for testing, but our learning path is designed for learning.

Okay, the first section is core data concepts. You’ll need to know the difference between batch and streaming data, what relational data looks like, visualization concepts, data analytics techniques, and the fundamentals of data processing.

The next section is about how to work with relational data on Azure. You’ll need to know some basics about relational data, such as tables, indexes, and views, but this section is mostly about Azure’s relational database services. The most important service in this section is Azure SQL Database, which is essentially a cloud-native version of Microsoft SQL Server, although it isn’t 100% compatible. For higher compatibility but fewer of Azure’s management benefits, you can use either Azure SQL Managed Instance or SQL Server on a virtual machine.

Another important relational service is Azure Synapse Analytics, which was formerly known as Azure SQL Data Warehouse. It allows you to query massive amounts of data. Microsoft also supports the most popular open source relational databases, including MySQL, MariaDB, and PostgreSQL.

The third section is about how to work with non-relational data on Azure. This section is almost entirely about Cosmos DB, which is Microsoft’s globally distributed database service. It’s also a multi-model database, which means that it supports a variety of database models using different APIs, including SQL, MongoDB, Cassandra, Table, and Gremlin. This section also covers different types of Azure Storage, including Blob Storage, File Storage, and Table Storage.

The final section is about analytics workloads on Azure. You’ll need to know about the various services involved in data warehousing, data processing, and visualization. As I mentioned earlier, Azure Synapse Analytics is Microsoft’s data warehouse service. It can also be used for data processing through its integration with Apache Spark, which is an open source data processing system. Spark jobs can also be run in Azure Databricks and Azure HDInsight. Azure Data Lake Storage is Microsoft’s solution for storing non-relational data for Spark.

Another important service in this section is Azure Data Factory. It lets you create a data processing pipeline that ingests data from different sources, transforms it, and loads it into other services.

Finally, you’ll need to know how to use Power BI to create reports and dashboards.

Now if you’re ready to learn the fundamentals of data solutions on Azure, then let’s get started!

To get to the next course in this learning path, click on the Learning Path pullout menu on the left side of the page. But please remember to rate this introduction before you go on to the next course. Thanks!