DP-900 prep: Microsoft Azure Data Fundamentals

Explore how Azure functions as a cloud computing platform offering compute, storage, and hundreds of services, including data management, databases, and analytics, with on-prem integration and a try-before-you-buy model.

Create a free Azure account and access 12 months of services, 200 dollars, and 25 plus always free options. Navigate cost management and quickstart dashboards to control expenses.

Create an Azure SQL database with a new server and resource group, enable firewall access for my IP, load AdventureWorksLT data, and run a select query via the query editor.

Master select statements in Azure sql and other azure databases, using from, where, group by, having, and order by; apply aggregates and aliases like count(city).

Discover Azure Data Studio as a lightweight, cross-platform tool for querying relational data on Windows, Mac, and Linux, and compare it to SQL Server Management Studio.

Compare SSMS and Azure Data Studio to choose the right tool for querying versus administration, security, and cross-platform use with varied export formats.

Explore how normalisation turns demoralised data into a clean relational structure by separating classes and teacher details into separate tables, reducing duplicates and simplifying queries.

Explain how primary keys uniquely identify rows and foreign keys link related tables, with examples of pupil ID and cascading updates.

Explore how indexes speed data retrieval using clustered and non-clustered structures, why primary keys define a clustered index, and how over-indexing slows updates.

Explore relational data structures in DP-900, focusing on tables, indexes, views, and constraints (not null, default, check, unique, primary and foreign keys) that enforce integrity; learn stored procedures and functions.

Compare on-prem and cloud options by examining IaaS, PaaS, and SaaS, and the implications for data location and regional control.

Explore IaaS, PaaS, and SaaS delivery models in Azure, from on-prem hardware to cloud databases, and see how responsibilities and capital expenditure shift.

Explore Azure SQL Database fundamentals, including fully managed backups, hyperscale, and intelligent threat protection. Create a new database, choose single or elastic pool, and configure server, resource group, and location.

Explore Azure SQL Database elastic pools for shared compute and storage across databases, with DTUs or vcore options, and serverless or provisioned deployment modes.

Explore Azure SQL database fundamentals, including backups, geo-replication, failover, encryption options and availability zones, plus access via SSMS, Visual Studio, and Azure Data Studio with a 99.99% SLA.

Examine the pricing and capabilities of Azure SQL managed instance, including vCore models, hybrid benefit, and up to a hundred databases, with high compatibility and lift-and-shift migration.

Learn how to run SQL Server on an Azure Virtual Machine (IaaS) to achieve 100% compatibility with on-prem, including setup, network configuration, and SQL management with SSMS.

Describe Azure database for PostgreSQL options, including single server and hyper-scale Citus, with read-only replicas, geo-redundant backups, and migration via Azure Database Migration Service.

Explore Azure database for MariaDB, compatible with Oracle and MySQL tools, using MySQL Workbench, fully managed, scalable compute and storage, backups, point-in-time restore, read replicas, and migration support.

Identify the right relational data offering for your workload by comparing SQL database, managed instance, and virtual machine options, plus PostgreSQL, MySQL, and MariaDB with high availability and elastic scaling.

Identify the responsibilities of database administrators, data engineers, and data analysts, from capacity planning and installation to data pipelines, cleansing, transforming, modelling, and security.

Explore non-relational data, focusing on semi-structured data like JSON and key-value formats, and learn how Azure Cosmos DB supports Core SQL API, MongoDB API, Table API, Cassandra, and Gremlin APIs.

Learn to replicate data globally across regions, enable multi-region writes, manage read and write costs, and choose from five consistency options to balance availability and latency.

Explore the Table, Cassandra, and MongoDB APIs in Azure Cosmos DB, covering NoSQL data with partition keys, column families or documents, and throughput options such as auto scale or manual.

Compare Cosmos DB API options to select the right data store, with a focus on Core SQL for new databases, and manage consistency, throughput, and multi-region configurations.

Explore Azure storage replication options such as lrs, zrs, grs, ra-grs, and gzrs, and see how standard and premium tiers influence regional availability.

Describe Azure table storage as a scalable, schema-less table with partition key and row key. Cosmos DB Table API offers faster indexing; consider Core SQL API for key-value models.

Explore Azure blob storage, including block, append, and page blobs; create block blob storage and containers, and upload images, videos, and website data.

Connect to Azure Storage Explorer, switch a container from private to public, and apply hot, cool, or archive access tiers with rehydration. Learn blob types and related costs.

Explore azure file storage: create a premium file storage in East US, set up a myfiles share via SMB or NFS, with encryption and LRS, ZRS, GRS, GZRS replication.

A transaction is a complete, separate unit of work in OLTP, begun with begin transaction and completed by commit or rollback, with normalization speeding writes but lengthening reads.

Explore data ingestion, processing, and querying in data warehousing, with sources ranging from banking to IoT and websites, and distinguish batch versus streaming data.

Compare batch and streaming data, showing how batch collects data over a period and processes it after completion, while streaming handles real-time, small, instant processing.

Differentiate transactional and analytical workloads by examining how OLTP writes and OLAP reads data from a data warehouse, supporting ingestion, transformation, and business intelligence visualizations.