DP-900 Microsoft Azure Data Fundamentals
Articles covering Microsoft Azure Data Fundamentals (DP-900) exam objectives. These guides focus on core data concepts including relational and non-relational data, transactional and analytical workloads, Azure SQL databases, Azure Cosmos DB, Azure Storage, and modern data analytics services.
DP-900 Objective 1.1: Describe Ways to Represent Data
Master data representation fundamentals including structured data with fixed schemas and tables, semi-structured data with flexible organization like JSON and XML, and unstructured data without inherent structure. Learn when to use each type and appropriate Azure storage services.
DP-900 Objective 1.2: Identify Options for Data Storage
Learn common data file formats including CSV, JSON, Parquet, and Avro. Understand database types including relational databases with ACID transactions, and NoSQL databases including key-value, document, column-family, and graph databases for diverse workload requirements.
DP-900 Objective 1.3: Describe Common Data Workloads
Master transactional workloads (OLTP) with ACID properties, normalized schemas, and low latency requirements, and analytical workloads (OLAP) with complex aggregations, denormalized schemas, and historical analysis. Learn ETL processes and appropriate Azure services for each workload type.
DP-900 Objective 1.4: Identify Roles and Responsibilities for Data Workloads
Learn database administrator responsibilities for security, performance, backups, and availability; data engineer responsibilities for ETL pipelines, data architecture, and data quality; and data analyst responsibilities for insights, visualization, and business intelligence.
DP-900 Objective 2.1: Describe Relational Concepts
Master relational database fundamentals including tables, rows, columns, primary keys, and foreign keys; normalization preventing data anomalies through 1NF, 2NF, and 3NF; SQL statement categories with DDL, DML, and DQL; and database objects including views, indexes, stored procedures, and triggers.
DP-900 Objective 2.2: Describe Relational Azure Data Services
Learn Azure SQL Database for fully managed PaaS databases with serverless compute and elastic pools; Azure SQL Managed Instance for near-complete SQL Server compatibility and lift-and-shift migrations; SQL Server on Azure VMs for maximum control; and open-source options including Azure Database for PostgreSQL, MySQL, and MariaDB.
DP-900 Objective 3.1: Describe Capabilities of Azure Storage
Master Azure Blob storage for unstructured object data with hot, cool, and archive access tiers and block, append, page blob types; Azure File storage for managed SMB/NFS file shares with Azure File Sync; and Azure Table storage for NoSQL key-value data with partition and row keys at massive scale and low cost.
DP-900 Objective 3.2: Describe Capabilities and Features of Azure Cosmos DB
Learn Azure Cosmos DB's globally distributed, multi-model NoSQL database with guaranteed low latency, elastic scaling, and comprehensive SLAs. Understand five APIs: NoSQL for documents, MongoDB for compatibility, Cassandra for wide-column, Gremlin for graphs, and Table for key-value with premium features.
DP-900 Objective 4.1: Describe Common Elements of Large-Scale Analytics
Master large-scale analytics including batch vs streaming ingestion, ETL vs ELT processing, data warehouses with structured schemas, data lakes with raw data, Azure Synapse Analytics as unified platform, Azure Databricks for Spark and ML, and Microsoft Fabric as integrated SaaS analytics.
DP-900 Objective 4.2: Describe Consideration for Real-Time Data Analytics
Learn differences between batch data (scheduled processing) and streaming data (continuous real-time processing). Understand Microsoft real-time analytics services: Azure Stream Analytics for SQL-based processing, Event Hubs for high-throughput ingestion, IoT Hub for device management, and Data Explorer for fast log analytics.
DP-900 Objective 4.3: Describe Data Visualization in Microsoft Power BI
Master Power BI capabilities including Desktop for authoring, Service for collaboration, and Mobile for access. Understand data models with tables, relationships, calculated columns, and DAX measures. Learn appropriate visualizations: bar/column for comparisons, line for trends, pie for composition, scatter for relationships, and maps for geography.