Quick Introduction guide to DP-900 Azure Data Fundamentals

Data defines a collection of facts used in decision making. Relational data organizes into tabular form with a rigid structure, while non-relational data uses a flexible schema.

Data workloads distinguish transactional relational systems for processing and analytical read-only systems for insights, highlighting consistency, isolation, and durability across operations, plus four stages: ingestion, transformation, storage, reporting.

Batch processing collects data into groups and processes them on a schedule or when triggered, while streaming processing handles data as it arrives for real-time ingestion and responses.

Explore three core data roles, including database administrators who ensure data availability and backups, and data analysts who build dashboards and reports across on-premise and cloud environments.

Define the data type and provision the appropriate database system. Explore on premise and cloud provisioning with infrastructure as a service, platform as a service, and software as a service.

Relational data uses a structure where entities are described by attributes in columns; primary keys identify rows, foreign keys link tables, while views and queries use where clauses and joins.

Explore relational azure data services, including infrastructure as a service and platform as a service, with single databases, elastic pools, scalability, security, encryption, and backups.

Explore sql basics, including dml and ddl, with examples of select, insert, update, delete, create, alter, drop, and rename statements. Understand joins and primary and foreign keys for relational queries.

Explore non-relational data types, including key-value, document-based, and column-family stores, with unique identifiers, hierarchical documents, and edge-based relationships enabling fast analysis.

Explore non-relational Azure data storage, including block, page, and append blobs, and containers. Learn about lifecycle policies, geo-replication, and Cosmos DB document storage with MongoDB, Cassandra, and Gremlin APIs.

Learn how data analytics transform and store data in warehouses, describe and diagnose past performance, predict future trends, and prescribe actions through historical and real-time insights.

Azure Data Factory enables cloud-based data integration, orchestrating data movement and transformation at scale with pipelines, activities, linked services, and datasets.

Explore data by creating visualizations such as charts and graph lines to analyze information, then assemble datasets from multiple sources into tiles, reports, and dashboards.

this lecture wraps up by directing learners to the Microsoft page for DP-900 Azure data fundamentals to access all the information.