Exam DP-900 Microsoft Azure FULL COURSE (UPDATED)

Explore core data concepts and formats that encode information, from JSON and txt to images and videos, and understand structured, semi-structured, and unstructured data.

Demonstrate how a toy company uses structured databases for inventory, semi-structured JSON feedback, and unstructured social media text, with guidance on storage, querying, and analysis.

Explore the three data formats—structured, semi-structured, and unstructured—through practical examples, from SQL databases and spreadsheets to API JSON and social media multimedia, showing when to store which data type.

Learn how to store data with local and cloud options, review common file types, and explore Azure Blob Storage for unstructured data and basic batch vs stream data concepts.

Explore how structured data is stored in databases as rows and columns, with connected tables and schemas enabling easy queries; compare to unstructured data and blob storage.

Explore four fundamental database types: relational, NoSQL, time series, and graph. Understand when each is used, how data is structured or unstructured, and how they relate to DP-900 exam scenarios.

Explore how transactional data drives business insights, from sales patterns to fraud monitoring, and master OLTP, CRUD operations, and data consistency for scalable databases.

Explore data roles like database administrator, data engineer, and data analyst, detailing responsibilities such as building and securing data infrastructure, ensuring integrity, and enabling analytics and scalable pipelines.

Explore Azure data resources and services for storage, analytics, and visualization across structured, semi-structured, and unstructured data; coveringAzure SQL, open source relational databases, Cosmos DB, and blob containers.

Learn how relational data on Azure uses tables, primary keys, and foreign keys to build a connected database schema, illustrated by a toy company example.

Explore how relational databases use tables and primary and foreign keys to form a normalized schema, applying atomicity, single value per cell, data type consistency, and unique column names.

Practice hands-on with MySQL Workbench to create a toy company schema and tables, define primary and foreign keys, and validate data flow for DP-900 preparation.

This lecture introduces SQL as a structured query language for relational databases, covering DDL, DML, and DCL with hands-on examples of creating, altering, inserting, querying, and granting access.

Explore database objects beyond tables, including indexes, views, stored procedures, functions, and triggers, and learn how queries operate on these objects to insert and update data.

Discover how Azure relational databases in the cloud deliver fully managed services, high availability, scalability, cross-platform compatibility, global distribution, and integrated monitoring and cost management for SQL workloads.

Discover how a toy company uses Azure SQL Database and related services to unify inventory, order processing, supply chain, CRM, and analytics across cloud.

Explore Azure's open source relational databases—MySQL, MariaDB, and PostgreSQL—plus Azure SQL Database and Cosmos DB, and learn licensing, compatibility, and cloud features like automatic backup and migration.

Learn how non-relational databases on Azure handle large-scale, unstructured and semi-structured data beyond tables, using document, key-value, column family, and graph models for real-time analysis.

Explore non-relational databases, designed for evolving data sets and high performance, storing unstructured or semi-structured data without traditional tables, and scalable by adding servers, with Azure's managed non-relational data services.

Explore Azure's managed non-relational data services and storage, including blob, page, and append blocks. Learn to organize data in containers with access policies for secure, scalable storage.

Explore Azure storage tiers hot, cool, and archive, their access patterns, latency, and cost implications, and the rehydration process that makes archived data usable.

Explore Azure data lake storage Gen2, a big data analytics platform with hierarchical storage, security and access control, offering hot, cool, and archive tiers for cost-efficient data analysis.

Explore Azure Files, a fully managed cloud file share service with a hierarchical folder structure accessible via SMB and NFS. Use SMB for Windows-dominated environments; use NFS for Linux workloads.

Explore Azure Table storage as a flexible NoSQL solution with a key-value model, partition and row keys, enabling scalable, schema-less storage of JSON or unstructured data.

Explore Azure storage options—blob for unstructured data, Azure Data Lake Storage Gen2 for big data analytics, file for shared access, and table for structured data.

Discover Azure Cosmos DB, a globally distributed, multi-model, schema-less non-relational database that supports documents, key-value, graph, and more for scalable, low-latency data access and real-time analysis.

Explore azure cosmos db's global distribution, low latency, and multi-modal data models. Compare its benefits with azure storage for a global e-commerce app.

Explore how Azure Cosmos DB supports multi-model data with five APIs—SQL API, MongoDB API, Table API, Cassandra API, and Gremlin API—across document, key-value, column family, and graph models.

Learn how data ingestion pipelines in Azure collect, process, store, and visualize data for Tesla, using Azure Blob Storage, Azure Databricks, Azure Functions, and Microsoft Fabric for real-time insights.

Explore how Microsoft Fabric unifies data ingestion, processing, enrichment, and visualization in Azure, using Tesla to illustrate dashboards, data models, and real-time insights from Azure Data Factory and Azure Databricks.

Explore data warehouse, data lake, and lake house, comparing structured, semi-structured, and unstructured data, and learn how lake house unifies analytics for real time insights.

Explore Microsoft Fabric and Azure Databricks for large-scale analytics, real-time insights, data pipelines, and flexible data storage across data lakes and warehouses.