Prepare for Microsoft Exam DP-900 and demonstrate your knowledge of fundamental data concepts and Microsoft Azure data services.
This Exam DP-900: Microsoft Azure Data Fundamentals video is designed to help you begin to work with data in the cloud. This video focuses on the skills measured by the exam objectives, as updated by Microsoft in April 2021.
Describe core data concepts
Describe how to work with relational data on Azure
Describe how to work with non-relational data on Azure
Describe an analytics workload on Azure
Using his years of experience working with data and teaching data best practices to a variety of learners, Microsoft Certified Trainer Chris Sorensen explains core concepts related to working with data in Azure. In his engaging style grounded in real-world scenarios, Chris gives you insights to relational and non-relational data, different types of workloads, and analytical best practices. With Chris as your guide, you are well-equipped to successfully attempt the DP-900 exam and advance in your career as a data engineer or database administrator.
Beginner to Intermediate
Who Should Take This Course
Certification candidates preparing for exam DP-900 Microsoft Azure Data Fundamentals
Analysts or admins beginning to work with data in the cloud
Foundational knowledge of core data concepts
About Pearson Video Training
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Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.
Table of Contents
Module 1: Describe Core Data Concepts Lesson 1: Describe Types of Core Data Workloads Learning objectives 1.1 Describe batch data 1.2 Describe streaming data 1.3 Describe the difference between batch and streaming data 1.4 Describe the characteristics of relational data Lesson 2: Describe Data Analytics Core Concepts Learning objectives 2.1 Describe data visualization 2.2 Describe basic chart types 2.3 Describe analytics techniques 2.4 Describe ELT and ETL processing 2.5 Describe the concepts of data processing
Module 2: Describe How to Work with Relational Data on Azure Lesson 3: Describe Relational Data Workloads Learning objectives 3.1 Identify the right data offering for a relational workload 3.2 Describe relational data structures Lesson 4: Describe Relational Azure Data Services Learning objectives 4.1 Describe Azure SQL family of products 4.2 Describe Azure Synapse Analytics 4.3 Describe Azure Database for PostgreSQL, Azure Database for MariaDB, and Azure Database for MySQL Lesson 5: Identify Basic Management Tasks for Relational Data Learning objectives 5.1 Describe provisioning and deployment of relational data services 5.2 Describe method for deployment 5.3 Identify data security components 5.4 Identify basic connectivity issues Lesson 6: Describe Query Techniques for Data Using SQL Language Learning objectives 6.1 Compare Data Definition Language (DDL) versus Data Manipulation Language (DML) 6.2 Query relational data in Azure Module 3: Describe How to Work with Non-Relational Data on Azure Lesson 7: Describe Non-Relational Data Workloads Learning objectives 7.1 Describe the characteristics of non-relational data 7.2 Describe the types of non-relational and NoSQL data 7.3 Recommend the correct data store 7.4 Determine when to use non-relational data Lesson 8: Describe Non-Relational Data Offerings on Azure Learning objectives 8.1 Identify Azure data services for non-relational workloads 8.2 Describe Azure Cosmos DB APIs 8.3 Describe Azure Table Storage 8.4 Describe Azure Blob Storage 8.5 Describe Azure File Storage Lesson 9: Identify Basic Management Tasks for Non-Relational Data Learning objectives 9.1 Describe provisioning and deployment of non-relational data services 9.2 Describe method for deployment 9.3 Identify data security components 9.4 Identify basic connectivity issues 9.5 Identify management tools for non-relational data Module 4: Describe an Analytics Workload on Azure Lesson 10: Describe Analytics Workloads Learning objectives 10.1 Describe transactional workloads 10.2 Describe the difference between a transactional and an analytics workload 10.3 Describe the difference between batch and real time 10.4 Describe data warehousing workloads 10.5 Determine when a data warehouse solution is needed Lesson 11: Describe the Components of a Modern Data Warehouse Learning objectives 11.1 Describe Azure data services for modern data warehousing 11.2 Describe modern data warehousing architecture and workload Lesson 12: Describe Data Ingestion and Processing on Azure Learning objectives 12.1 Describe common practices for data loading 12.2 Describe the components of Azure Data Factory 12.3 Describe data processing options Lesson 13: Describe Data Visualization in Microsoft Power BI Learning objectives 13.1 Describe the workflow in Power BI 13.2 Describe the role of interactive reports 13.3 Describe the role of dashboards 13.4 Describe the role of paginated reporting Summary